Stuart Cheetham, CEO at MPowered Mortgages, on how AI-powered technology allows mortgage lenders to fully underwrite loan applications in minutes

AI technologies are about to have a huge impact on the mortgage market… In November last year the founders of Revolut announced plans to launch a “fully digital, instant” mortgage in Lithuania and Ireland in 2025. Details were sketchy but the company said that mortgages will be part of a “comprehensive credit offering” it intends to build.

Neobanking progress with AI

Digital only banks, like Revolut and Monzo, are renowned for using the power of technology and data science to create efficiencies and improve customer experience. The reason neobanks have been so successful is because they provide a modern, convenient and cost-effective alternative to traditional banking. This is done a transparent way, through fast onboarding, 24/7 app access and instant notifications. All with a user-friendly interface.

While many financial services sectors have embraced financial technology in the way Revolut and Monzo have for the retail banking sector, the mortgage sector has struggled to make a real breakthrough here. Why hasn’t the mortgage industry caught up one might ask? Mortgages are complex financial products, existing at the intersection of justifiably stringent regulation. They represent the single biggest financial commitment people make in their lifetimes. Financial advisors who source mortgages on behalf of borrowers are hindered at every stage by outdated systems and inadequate or commoditised product offerings.

Disrupting the Mortgage Market

The mortgage industry is one financial services sector that has been yearning to be shaken up by the FinTech industry for some time. While it’s encouraging to see a successful brand like Revolut enter this market, what is less known is that huge progress is being made already by smaller and less well known FinTech disruptors.

For example, the mortgage technology company MQube has developed a “new fast way” of delivering mortgage offers using the cutting edge of AI technology and data science. Today, it still typically takes several weeks to get a confirmed mortgage offer. This is one of the major reasons the homebuying process can be so time consuming and stressful for brokers and borrowers. The mortgage process is characterised by bureaucracy, paperwork, delays and often frustratingly opaque decision-making by lenders. This leads to stress and uncertainty for consumers, and their advisors. And at a time when they have plenty of other property-purchase related challenges to contend with.

Our proprietary research shows us, and this will come as no surprise, that the biggest pain point for borrowers and brokers about the mortgage process is that it is time consuming, paperwork heavy and stressful. Imagine a world where getting a mortgage is as quick and as easy as getting car insurance. This is MQube’s vision.

MQube – AI-powered Mortgages

MQube‘s AI-powered mortgage origination platform allows mortgage lenders to fully underwrite loan applications in minutes. MPowered Mortgages is MQube’s lending arm and competes for residential business alongside the big banks. It uses MQube’s AI-driven mortgage origination platform and is now able to offer a lending decision within one working day to 96% of completed applications.

The platform leverages state-of-the-art artificial intelligence and machine learning to assess around 20,000 data points in real-time. This enables lenders to process mortgage applications in minutes, transforming the industry standard of days or weeks. It automates the entire underwriting journey, from application to completion. This helps to provide a faster service, reduce costs, mitigate risks, and to make strategic adjustments quickly and effectively. By assessing documents and data in real-time during the application, it is able to build a clearer and deeper understanding of a consumers’ circumstances and specific needs. Applicants are never asked questions when MQube can independently source and verify that data, leading to a streamlined and paperless experience. Furthermore, this whole process reduces dependency on human intervention.

The benefits of AI

More and more lenders are seeing the benefits AI and financial technology can bring to their business. They are beginning to adopt such AI-driven financial systems which are scalable and serve to address systemic problems in this industry. The mortgage industry is still some way behind the neobanks, but what’s hugely exciting to see is the progress that has been made so far. Moreover, if FinTechs continue to innovate this sector and if lenders continue to embrace financial technology and use at scale, then getting a mortgage could genuinely become a quick, easy and stress free process. At this point, the mortgage industry could begin to see a shift in consumer perception and change in consumer behaviour. A new frontier for the mortgage industry is upon us.

  • Artificial Intelligence in FinTech
  • Neobanking

Glenn Fratangelo, Head of Fraud Product Marketing & Strategy at NICE Actimize, on financial services fraud prevention in 2025.

2024 marked a turning point in financial crime management with the advent of Generative AI (GenAI). McKinsey estimates GenAI could add a staggering $200-340 billion in annual value to the global banking sector. A potential revenue boost of 2.8 to 4.7%. This underscores the transformative potential of GenAI. IT IS rapidly evolving from a futuristic concept to a powerful tool in the fight against financial crime. However, 2024 was just the prelude. 2025 promises to be the year GenAI truly comes into its own. Unlocking transformative capabilities in combating increasingly sophisticated threats. 

This evolution is not merely desirable, it is essential. The Office of National Statistics (ONS) reported a concerning 19% year-over-year increase in UK consumer and retail fraud incidents in 2024, reaching approximately 3.6 million. This stark reality underscores the urgent need for financial institutions (FIs) and banks to bolster their defences against financial crime. In 2025, leveraging the power of GenAI is no longer a luxury, but a necessity for protecting customers and safeguarding the financial ecosystem. 

The evolving GenAI-powered fraud landscape

Fraudsters have embraced GenAI as a potent weapon in their arsenal. This technology’s ability to create realistic fakes, automate attacks and mimic customers creates a significant threat to the financial landscape.

Deepfake technology has become a particularly insidious tool. By generating highly realistic voice and facial fakes, fraudsters can bypass remote verification processes with ease. This opens doors to unauthorised access to sensitive information, enabling account takeovers and other fraudulent activities.  

In addition, the rise of synthetic identities further complicates the challenge. By blending real and fabricated data, fraudsters can create personas that seamlessly infiltrate legitimate customer profiles. These synthetic identities are extremely difficult to detect, as they appear indistinguishable from genuine customers. Making it challenging for institutions to differentiate between legitimate and fraudulent activities.

Phishing scams have also undergone a dramatic evolution, becoming more sophisticated and personalised. AI-driven techniques allow fraudsters to craft personalised, convincing emails that mimic legitimate communications, resulting in significant data breaches.

Harnessing GenAI

GenAI is being used by criminals – presenting a significant challenge in the realm of fraud. It requires advanced AI capabilities such as real-time behavior analytics that use machine learning to continuously analyse all entity interaction and transaction patterns. This can identify subtle deviations from a customer’s typical behaviour. It allows for initiative-taking and the flagging of suspicious activity before any damage occurs. Moreover, providing a significant advantage over traditional, rigid rule-based systems that often fail to detect nuanced threats.

Fraud simulation and stress testing using GenAI can also empower institutions to proactively assess the resilience of their systems. By simulating potential fraud scenarios, financial institutions can identify vulnerabilities and train detection models to recognise emerging tactics. Furthermore, this proactive preparation ensures that defences remain ahead of fraudsters’ evolving methods, creating a more robust and adaptable security infrastructure.

Low volume high value fraud, such as BEC or other large value account to account transfers usually lack the quantity of data needed to optimise models. GenAI can address this by creating synthetic data that mimics real-world scenarios. This approach significantly improves the accuracy and robustness of detection models, making them more effective against new and unforeseen threats.

GenAI has the potential to transform the investigation process by automating tasks such as generating alerts and case summaries, as well as SAR narratives. This automation not only minimises errors but also frees analysts from mundane tasks, allowing them to focus on higher-value activities. The result is a significantly accelerated financial crime investigation process, enabling institutions to respond to threats with greater speed and efficiency.

The battle against fraud in 2025 and beyond

The battle against financial fraud in 2025 and beyond is an undeniable arms race. Fraudsters, wielding generative AI as their weapon, will relentlessly seek to exploit vulnerabilities. To counter this evolving threat, financial institutions must embrace AI to outmanoeuvre fraudsters and proactively protect their customers.

The future of fraud and financial crime prevention hinges on our ability to innovate and adapt. Institutions that view GenAI not just as a challenge, but as an opportunity, will emerge as leaders in this fight. AI is a force multiplier for institutions striving to combat fraud and financial crime, empowering them with smarter, faster, and more adaptive defences, we can create a more secure and trustworthy financial ecosystem. The choice to innovate in the face of adversity will define the path forward and shape the future.

  • Artificial Intelligence in FinTech

Paul O’Sullivan, Global Head of Banking and Lending at Aryza, on the rise of AI in banking

The banking sector stands at the crossroads of technological innovation and operational transformation. AI is taking centre stage in reshaping how financial institutions operate. The banking sector is beginning to recognise AI’s potential. It can address challenges, enhance operational efficiency, and deliver more personalised customer experiences.

The Current State of AI in Banking

Research reveals that while a number of banking organisations have yet to fully integrate AI into their operations, key areas such as debt recovery are leading the charge. The slower pace of adoption can be attributed to the highly regulated environment of banking. Because transparency, compliance, and customer trust are non-negotiable. However, despite this cautious approach, banks that have implemented artificial intelligence are already seeing significant benefits, particularly in risk management.

AI’s Role in Risk Management

Effective risk management is a cornerstone of the banking sector. AI is proving to be a powerful tool in this area. By analysing vast amounts of data and providing predictive insights, AI enables banks to mitigate risks early. They can strengthen customer portfolio stability, and make data-driven lending decisions. These capabilities are essential in a landscape where financial risks can escalate rapidly.

Beyond the expected benefits, banks have also reported enhanced customer insights as an unexpected advantage. By leveraging AI to analyse customer behaviours and preferences, banks can tailor their products and services more effectively. Furthermore, they can improve customer satisfaction and experience, whilst fostering long-term loyalty.

Challenges to Adoption

Although organisations are experiencing a multitude of advantages, the integration of AI in banking is not without its hurdles. Legacy IT systems, stringent regulatory requirements, and concerns around data privacy pose significant challenges to widespread adoption. Banks must ensure AI-driven decision-making processes are effective. Moreover, they must also be fully transparent and compliant with industry regulations. Further highlighting the importance of a gradual, strategic approach to AI implementation.

Opportunities Ahead

The potential for AI in banking extends far beyond risk management. From streamlining operational workflows to enhancing customer personalisation and improving decision-making. AI is set to drive innovation across the sector. For example, AI-powered chatbots and virtual assistants transform customer service by providing instant, 24/7 support. They can handle complex interactions, enhancing customer satisfaction. At the same time, advanced analytics enable banks to analyse behaviour patterns, predict trends, and personalise product offerings. Furthermore. enhancing cross-selling opportunities and driving deeper customer engagement. These tools are becoming strategic enablers for innovation in the financial landscape.

A Call to Action

For banks to fully realise the benefits of AI, they must address the digital transformation gap, modernising outdated infrastructures and fostering a culture of innovation. This includes investing in technologies that align with their strategic goals, ensuring robust data security measures alongside maintaining compliance with evolving regulations.

As the banking sector continues its journey towards digital maturity, AI will play a pivotal role in defining its future. By overcoming current barriers and embracing AI-driven solutions, banks can not only enhance operational efficiency but also deliver the seamless, personalised experiences that customers now expect in an increasingly digital world.

About Aryza

At Aryza know that in today’s highly regulated world, there is huge value in quickly guiding your customers through the product that best fit their immediate needs, through a seamless journey that is tailored to their specific circumstances.

We created smart platforms, responsible and compliant products, and a unique system of companies and capabilities so that businesses can optimise their customers’ journey through the right product at the right time.

For our teams across the globe, the growth of Aryza is a good news story and a testament to our clear vision and goals as an international business.

And also front of mind as we build a global footprint is our impact on the environment. Aryza is committed to reducing its carbon impact through the choices it makes and we are pleased to say that we follow an active roadmap.

  • Artificial Intelligence in FinTech

Jamil Jiva, EVP at Linedata, on compliance in asset management following the EU AI act

AI’s value-add has shifted from speculative to tangible in recent years. For consumers, it’s brought convenience; for businesses, invaluable timesaving. In the asset management space however, its impact is transformative. It can help assess choice, trust, and risk in seconds. AI isn’t just improving efficiency, it’s fundamentally reshaping decision-making processes.

It’s clear artificial intelligence is achieving widespread adoption among asset managers. Linedata’s recent global survey showed that 36% of asset management companies have already integrated AI into their operations. A further 37% are preparing to introduce it.

However, adopting new and evolving technology can prove to be a long-term challenge. Asset managers have to adapt to regulation as it changes. For example, the newly enacted EU AI Act is designed to regulate high-risk uses. It seeks to ensure safety, transparency, and accountability. With new regulations arriving thick and fast, companies should avoid rushing their implementation or cutting corners. Compliance should be their first and last thought.

AI can bring immediate benefits in optimising efficiency, streamlining operations, and boosting decision-making capabilities. The newly enacted EU AI Act will push firms planning to take a more measured approach to deploying artificial intelligence. This will necessitate a long-term, compliance-driven approach.

The New Compliance Landscape

The EU AI Act marks a turning point for AI governance. For the financial sector, the act will put explainability at the fore of AI-augmented decisions. For asset management firms, which increasingly rely on AI to drive decisions related to market forecasts, risk modelling, and portfolio management, the act mandates a robust approach to accountability.

Asset management firms that use AI must now prioritise governance or risk severe penalties and long-term reputational damage. As firms adjust to the EU AI Act, they must recalibrate their AI strategies and implement future-proof frameworks that blend innovation with security and ethical standards.

Hybrid AI Systems: Creativity and Control

One promising approach to the new regulatory environment is hybrid AI. Hybrid systems marry proprietary data with third-party models. With a blended strategy firms retain full oversight over sensitive tasks – such as decision-making models . Meanwhile, outsourcing less critical functions like data analysis or back-office automation to third-party vendors.

However, hybrid systems bring their own challenges under the EU AI Act. The new regulation imposes strict requirements for transparency. This means firms must ensure that any external solutions they adopt meet the same high standards of risk management and documentation. This necessitates a more in-depth vetting process for third-party providers and ongoing oversight to guarantee compliance. Effective governance, therefore, hinges not just on internal processes but also on the integrity and transparency of external systems and partners.

Despite these complexities, hybrid AI presents an opportunity for asset managers to continue innovating without compromising on compliance. By carefully managing these systems, firms can position themselves to harness the full potential of artificial intelligence while mitigating the risks associated with regulatory breaches.

Building a Sustainable AI Strategy

While the EU AI Act certainly raises the bar for compliance, it also presents an opportunity for firms to create more sustainable, future-proof strategies. Much like how the GDPR transformed data governance, the AI Act could drive a more comprehensive approach to artificial intelligence oversight, encouraging firms to adopt stronger ethical frameworks while staying ahead of regulatory shifts.

For asset managers, investing in adaptable AI infrastructures is one way to navigate these regulatory demands. By focusing on systems that are both flexible and scalable, firms can ensure they remain compliant with evolving regulations without sacrificing the pace of innovation. In particular, areas like predictive analytics, ESG reporting, and portfolio management stand to benefit from such advancements, provided firms integrate transparency and accountability into their strategies.

Asset managers who view regulatory challenges as opportunities – rather than obstacles – will emerge as leaders, showcasing a commitment to ethical AI that can ultimately build trust with clients and regulators alike. While the EU AI Act may seem daunting at first, for those who embrace the changes, it offers a chance to redefine how artificial intelligence can shape the future of asset management.

  • Artificial Intelligence in FinTech

additiv, a global leader in fintech and digital transformation, has announced the launch of an InsurTech solution with AXA Switzerland

AXA Switzerland has successfully launched its addProtect bancassurance offering, powered by additiv’s technology platform. Furthermore, this innovative InsurTech solution allows banks to directly protect their mortgage customers against key risks with a simple plug-and-play solution.

addProtect InsurTech solution from additiv

As a seamless plug-and-play solution, addProtect gives banks direct access to the platform without the need for additional integration with existing IT systems. Its user-friendly and intuitive design allows banks to effortlessly integrate the platform into their day-to-day business operations. With the death and payment protection insurance, bank advisors have easy-to-understand products at their disposal. These offer added value to customers beyond the existing offering. The addProtect platform is now available for banks, and an initial pilot will be launched in collaboration with PostFinance.

Samuel Peter, Head of Partnerships at AXA Switzerland, stated:

“With addProtect, AXA is responding to the growing need of customers and banks for appropriate insurance solutions where and when they are needed. The solution creates additional advisory potential and better protection for the customers of our partners’ banks. We look forward to making the solution available to other partners.”

Dieter Lützelschwab, General Manager Switzerland at additiv, added:  

“When developing addProtect, we focused on the user experience for the customer and the bank advisor. In addition, our platform provides an easily configurable, modular insurance solution that covers the entire value chain from quotation to claims processing.”

About additiv

additiv empowers the world’s leading financial institutions and brands to create new business models and transform existing ones. additiv’s API-first cloud platform is one of the world’s most powerful solutions for wealth management, banking, credit, and insurance. The InsurTech technology, together with the global ecosystem of regulated financial services providers, opens up new opportunities for banks, insurance companies, asset managers, IFAs and consumer brands to quickly and flexibly offer their own and third-party financial solutions through existing or new customer channels.

Headquartered in Switzerland, with regional offices in Singapore, UAE, Germany, and the UK, and more than 250 employees, additiv serves over 400 financial institutions (banks, insurers, asset managers, pension providers, IFAs, etc.) and brands worldwide.

  • InsurTech

Scott Zoldi, Chief Analytics Officer at FICO considers whether the current AI bubble is set to burst, the potential repercussions of such an event, and how businesses can prepare

Since artificial intelligence emerged more than fifty years ago, it has experienced cycles of peaks and troughs. Periods of hype, quickly followed by unmet expectations that lead to bleak periods of AI-winter as users and investment pull back. We are currently in the biggest period of hype yet. Does that mean we are setting ourselves up for the biggest, most catastrophic fall to date?

AI drawback

There is a significant chance of such a drawback occurring in the near future. So, the growing number of businesses relying on AI must take steps to prepare and mitigate the impact a drawback or complete collapse could have. Research from Lloyds recently found adoption has doubled in the last year, with 63% of firms now investing in AI, compared to 32% in 2023. In addition, the same study found 81% of financial institutions now view it as a business opportunity, up from 56% in 2023.

This hype has led organisations to explore AI use for the first time. Often with little understanding of the algorithms’ core limitations. According to Gartner, in 2023 less than 10% of organisations were capable of operationalising AI to enable meaningful execution. This could be leading to the ‘unmet expectations’ stage of the damaging hype/drawback cycle. The all-encompassing FOMO of repeating the narrative of the incredible value of AI does not align with organisations’ ability to scale, manage huge risks, or derive real sustained business value.

Regulatory pressures for AI

There has been a lack of trust in AI by consumers and businsses alike. It has resulted in new AI regulations specifying strong responsibility and transparency requirements for applications. The vast majority of organisations are unable to meet these in traditional AI, let alone newer GenAI applications. Large language models (LLMs) were prematurely released to the public. The resulting succession of fails fuelled substantial pressure on companies to pull back from using such solutions other than for internal applications. It has been reported that 60% of banking businesses are actively limiting AI usage. This shows that the drawback has already begun. Organisations that have gone all-in on GenAI – especially those early adopters – will be the ones to pull back the most, and the fastest.

In financial services, where AI use has matured over decades, analytic technologies exist today that can withstand regulatory scrutiny. Forward-looking companies are ensuring they are prepared. They are moving to interpretable AI and backup traditional analytics on hand while they explore newer technologies with appropriate caution. This is in line with proper business accountability, vs the ‘build fast, break it’, mentality of the hype spinners.

Customer trust with AI

Customer trust has been violated by repeated failures in AI, and a lack of businesses taking customer safety seriously. A pull-back will assuage inherent mistrust in companies’ use of artificial intelligence in customer facing applications and repeated harmful outcomes.

Businesses who want their AI usage to survive the impending winter need to establish corporate standards for building safe, transparent, trustworthy Responsible AI models that focus on the tenets of robust, interpretable, ethical and auditable AI. Concurrently, these practices will demonstrate that regulations are being adhered to – and that their customers can trust AI. Organisations will move from the constant broadcast of a dizzying array of possible applications, to a few well-structured, accountable and meaningful applications that provide value to consumers, built responsibly. Regulation will be the catalyst.

Preparing for the worst

Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of algorithm mathematics and the very signifiicant risk in using the technology.

Stringing together AI is easy. Building AI that is responsible and safe is a much harder and exhausting exercise requiring model development and deployment corporate standards. Businesses need to start now to define standards for adopting the right types of AI for appropriate business applications, meet regulatory compliances, and achieve optimal consumer outcomes.

Companies need to show true data science leadership by developing a Responsible AI programme or boosting practices that have atrophied during the GenAI hype cycle which for many threw standards to the wind. They should start with a review of how regulation is developing, and whether they have the standards, data science staff and algorithm experience to appropriately address and pressure-test their applications and to establish trust in AI usage. If they’re not prepared, they need to understand the business impacts of potentially having artificial intelligence pulled from their repository of tools.

Next, these companies must determine where to use traditional AI and where they use GenAI, and ensure this is not driven by marketing narrative but meeting both regulation and real business objectives safely. Finally, companies will want to adopt a humble approach to back up their deployments, to tier down to safer tech when the model indicates its decisioning is not trustworthy.

Now is the time to go beyond aspirational and boastful claims, to have honest discussions around the risks of this technology, and to define what mature and immature AI look like. This will help prevent a major drawback.

  • Artificial Intelligence in FinTech

Alexandra Mousavizadeh, CEO and Co-Founder of Evident, on how global banks are stepping up their AI comms in the face of growing investor scrutiny

In the big banks’ Q2 earning calls this year, a critical milestone was reached. For the first time, half of the 50 major banks we track in the Evident AI Index fielded questions from equity analysts concerning risks and opportunities specific to artificial intelligence (AI).

External scrutiny of the banks’ AI progress is steadily increasing. This is in line with the huge sums institutions have pumped into originating, developing, rolling out and scaling AI use cases. Banking leaders we’ve spoken to aren’t expecting to register meaningful bottom line business impacts from AI investments for at least another 24-36 months. Meanwhile, investors need satisfying that progress is being made, and that ROI will be forthcoming,

Against this backdrop, the way in which banks communicate around AI is becoming increasingly important.

Just 12 months ago, many banks were making only sporadic, broad-brush or conceptual references to AI. However, our recent AI Leadership Report revealed every bank in the Evident AI Index now has a communications and marketing strategy. Furthermore, the majority are referencing AI across multiple communications channels. These include annual reports, press releases, company LinkedIn posts, and media interviews.

Banks need to ‘talk the walk’

It’s not just the volume of comms, but the substance that is increasing. More banks are now willing to reveal specifics around internal use cases already in production. Moreover, they are sharing the results of these efforts and tangible information about what they are doing to scale artificial intelligence.

Last year, only 6 of 50 Index banks identified AI as a strategic priority in investor relations materials, and clearly described specific use cases in production alongside their ROI. This year, this number increased 2.5x to 15 banks.

These substantive communications help to reassure and placate investors. Furthermore, if a bank is perceived to be at the leading edge of AI adoption, the easier it becomes to attract, retain and inspire the talent needed to make organisation-wide transformation a reality

The C-Suite needs to engage in the AI debate

To achieve cut through in the debate, banks are mobilising their C-level leaders to publicise their ongoing efforts. They are setting out their vision for becoming AI-first organisations.

Of the 50 banks, 45 now have at least one C-Suite executive that has engaged on the topic of AI in external media in the last year. Furthermore, 15 of the 50 banks have two spokespeople on AI, while six banks (CaixaBank, DBS, Goldman Sachs, Intesa Sanpaolo, JPMorgan Chase, and NatWest) are engaging with four or more spokespeople across the Executive team.

As the primary owner of the bank’s strategic vision, the CEO should arguably lead from the front when it comes to market communications around AI. Meanwhile, JPMorgan Chase leads the pack across a host of AI maturity metrics. The efforts of Jamie Dimon to set the agenda and relentlessly beat the drum should not be understated.

Over the past 12 months, Dimon has been quoted in the media on AI topics around 10x more than any other banking chief. He continuously reaffirming his institution’s dominant position in the eyes of investors. This is an intentional, coordinated AI communications strategy that other banks would be well advised to follow.

Communicating tangible AI gains is vital as operational realities bite

Every potentially game-changing new technology follows a well-established hype cycle. In the case of AI, we’re now seeing the inflated expectations of Generative AI – arguably the most significant technology innovation of the past decade – being tempered by the realities (and difficulties) of operationalisation.

A recent memo from leading venture capital firm Sequoia Capital highlighted the elephant in the room. Namely, that the gap between what’s being spent to build out AI (mostly by tech companies) and the actual revenue realised by that investment has risen to $600 billion this year, up from $200 billion in September 2023. Investors are starting to probe for detail on when and where the ROI is coming from and, like Big Tech, the world’s leading banks will find it impossible to duck the difficult questions.

A delicate balance must be struck. Overpromising on AI today and underdelivering further down the line could prove disastrous. And yet, banking leaders know that in the highly contested race for artificial intelligence supremacy, failing to communicate their plans and progress also carries reputational risk.

Of the 50 banks we track, 38 announced at least one AI use case in the last year. Meanwhile, only 21 reported any outcomes associated with those use cases. And of those, only two – JPMorgan Chase and DBS – went so far as to specify their total actual realised $ return on AI spend last year.

With investor scrutiny only likely to intensify in the year ahead, individuals at the top of every bank must set forth a clear vision. They must establish frameworks for measuring the effectiveness of their AI efforts and the ROI being realised. And, crucially, provide consistent and clear communication every step of the way.

  • Artificial Intelligence in FinTech

Nicholas Holt, Head of Solutions and Delivery, Europe, Marqeta on how AI has the potential to revolutionise payments

The financial services sector has witnessed a profound transformation over the past two decades. It has been propelled by technological advancements. From online banking to mobile-first platforms like Revolut and Monzo, the industry is continuously evolving. The integration of Artificial Intelligence (AI) into financial services is set to push the boundaries even further. Offering enhanced convenience and changing how we manage our money.

AI offers the ability to process and analyse vast amounts of data in real-time. It promises to make financial services intuitive, intelligent, and personalised to individual needs. And it can also help to make it more secure.

AI-Powered Personalisation

AI can interpret a consumer’s transaction history and spending patterns to create tailored financial recommendations. These include optimising payment methods, choosing better reward programmes, or suggesting savings opportunities. This degree of personalisation is far more sophisticated than the broad, one-size-fits-all approach currently offered by banks.

The technology can enable ‘predictive cards’ to leverage machine learning algorithms to set personalised credit limits and rewards based on an individual’s financial behaviour. By predicting future needs, AI-powered tools can offer a more holistic view of one’s finances. They can improve financial literacy and promote better financial decision-making.

Consumers are increasingly warming to the idea of AI in financial services. According to Marqeta’s 2023 Consumer Pulse Report, 36% of consumers in the US and the UK expressed interest in using AI tools to help manage their finances. This figure rose to over 50% for consumers under 50, indicating a clear demand for personalised AI-driven solutions.

Unlocking Access to Credit

Access to credit is a significant factor in financial inclusion. AI has the potential to expand this access by transforming how creditworthiness is assessed. Traditionally, credit approval processes have relied heavily on limited data points, such as a person’s credit score and income. However, AI can analyse a broader range of data, from spending patterns to social media behaviour. This can provide a more nuanced assessment of an individual’s creditworthiness.

By using advanced machine learning models, AI can process this data at incredible speeds. This allows more people to be approved for credit faster and with greater accuracy. It can be particularly beneficial for individuals who may have struggled to secure credit through traditional methods, such as younger consumers or those without a lengthy credit history.

Generative AI (GenAI), which builds upon traditional AI by predicting and creating entirely new behaviours and patterns, also holds promise in this area. As the use of GenAI tools grows, we can expect more tailored financial products that respond to each consumer’s unique needs. Moreover, this could include personalised loan offerings or dynamic credit options that adapt in real-time to a person’s financial situation.

Fighting Fraud

While personalisation is one of AI’s most exciting applications, its ability to detect and prevent fraud is another crucial benefit. Fraud detection is a near constant battle across financial services, with millions of transactions processed every minute across the globe. Identifying suspicious activities quickly and accurately is essential for maintaining trust and security.

Machine learning algorithms are adept at spotting irregularities that might be missed by human analysts or even traditional software. Additionally, these systems can identify patterns that indicate potential fraud and alert financial institutions instantly, allowing them to take swift action.

Furthermore, as fraud techniques evolve, AI systems will continuously learn and adapt, staying one step ahead of cybercriminals. This capacity to evolve will make AI an invaluable asset in the fight against fraud.

AI and Embedded Finance

Embedded Finance, the process of integrating financial services into non-financial platforms, has already begun reshaping how consumers and businesses interact with money. AI is set to accelerate this trend, enhancing the capabilities of embedded financial tools with real-time data processing and hyper-personalisation.

For instance, businesses could use AI-powered embedded finance solutions to offer tailored payment options at checkout based on a customer’s purchasing behaviour. This could include personalised financing options, such as Buy Now, Pay Later (BNPL) services, or optimised rewards based on previous transactions. Companies like Marqeta are already exploring AI’s potential to elevate embedded finance, making these interactions seamless and highly personalised.

The Future of Finance

Financial services in 20 or just 10 years from now will likely be unrecognisable compared to today. AI will play a central role in shaping this evolution. Consumers and businesses can expect a future where financial products are deeply integrated into everyday life. However, not as separate, standalone services, but as seamless, invisible enablers of transactions and financial management.

GenAI will become increasingly sophisticated, offering predictive insights that can help consumers manage finances with greater precision. For businesses, AI-driven solutions will enable more efficient operations, cost reductions, and enhanced customer engagement through personalised offerings.

In this future, consumers will enjoy unparalleled convenience and flexibility. Payments, credit, and financial planning will be customised to fit the individual, with AI continuously learning and adapting to offer better recommendations and insights. This will lead to greater financial literacy, broader access to credit, and improved financial security. Additionally, financial service providers will gain much greater control over fraud and other security challenges.

Fred Fuller, Global Head of Banking at Endava, on how banks can effectively communicate AI advancements and demonstrate ROI to investors

There is no single solution, AI or otherwise, that can prepare financial institutions for the modern world. To build a bank capable of successfully navigating the challenges of the future, a long-term digital transformation strategy is required. Especially relevant in the wake of recent IT outages,

At present, according to Endava’s Retail Banking Report 2024, 67% of banks are still heavily reliant on legacy systems. This leads to wasted budget and decreased efficiency. With limited resources available to modernise their tech stack, company leaders are often forced to choose which technology-type to prioritise. When doing this, 50% have chosen artificial intelligence (AI).

Is AI alone enough?

Can AI overhaul archaic processes or are there too many hurdles in the way? The first hurdle to successful digital transformation in financial services is overcoming the employees’ perception of the process. Time and time again, corporations have failed in the goal to integrate solutions that successfully feed into a long-term tech strategy. Often, this is due to wide-spread change fatigue. When exhausted by continuous efforts to change their day-to-day, workers become resistant to transformation. The best way to overcome change fatigue, and drive digital transformation in financial institutions, is through overhauling legacy systems. And adopting solutions that will stand the test of time.

Legacy Systems

Across the world, outdated legacy systems are holding financial institutions back and costing them billions. From 2022 to 2028, this expense is expected to grow at a rate of 7.8%. Not only do these archaic processes cost money, but they force banks to contend with a multitude of siloes. From departments to data. We live in a world where neobanks are growing in popularity. They are able to provide a frictionless customer experience using their modern tech stack. Traditional organisations must rid themselves of siloes to enable all areas of the business to leverage AI. In turn, this will provide them with strong data collection and support from departments who are agreed on next steps.

At present, three quarters of financial institutions feel they need to modernise their core. Without this change, they lack the secure, data-driven foundation necessary to utilise AI and see return on their technical investments.

The benefits of AI integration

Once a strong foundation has been laid, it becomes easier to see the practical benefits of integrating AI. For example, when data is no longer siloed by legacy systems, using chat bots to support customers with simple queries creates an efficient consumer experience. There are internal benefits too. AI can spot potentially suspicious activity, flagging it before it is too late. Or analysing data to ensure risk management and process automation. Despite its wide-reaching capabilities, AI alone is not the only option for financial institutions…

Routes to the future

Endava’s Retail Banking Report also showcased the variety of solutions that banks are using to improve their tech stack. 45% of respondents recognised data analytics, in and of themselves, as a top area for investment. Meanwhile 30% flagged IoT, and 14% the Metaverse.

There’s a reason for the emphasis on strong data. It not only supports the integration and use of AI-fuelled capabilities, but it is the driving force behind numerous functions of the bank itself. Of those surveyed, 37% aimed to use data to improve customer service. 34% to strengthen security, and 33% to personalise products and improve the customer experience.

As well as attracting and retaining consumers, business leaders can benefit from their access to strong data by attracting and retaining talent. With 39% of failed digital transformations viewing lack of employee buy-in as a factor, financial institutions are encouraged to educate workers on their technology integration plans, and ensure solutions are user-friendly. Fortunately, looking ahead, 20% of banks surveyed seek to use data to improve the workplace.  

A bank’s priority – looking ahead

More than ever, banks are reliant on data to keep operations running smoothly. From providing customers with a personalised experience to improving the workplace in the competition for talent, there are a multitude of reasons to ensure the foundations of your tech stack are strong.

Doing so makes integration of new technology a smoother experience for all. To this end, it’s no shock that 50% of banks are keen to embrace AI, using it to benefit customers and speed up processes. However, with many hampered by the legacy technology and the ever-looming threat of change fatigue, integration of any technology should be carefully planned, customer focused and data led.

  • Artificial Intelligence in FinTech

Gabe Hopkins, Chief Product Officer at Ripjar, on how GenAI can transform compliance

Generative AI (GenAI) has proven to be a transformational technology for many global industries. Particularly those sectors looking to boost their operational efficiency and drive innovation. Furthermore, GenAI has a range of use cases, and many organisations are using it to create new, creative content on demand – such as imagery, music, text, and video. Others are using the new tools at their disposal to perform tasks and process data. This makes previously tedious activities much more manageable, saving considerable time, effort, and finances in the process.

However, compliance as a sector has traditionally shown hesitancy when it comes to implementing new technologies. Taking longer to implement new tools due to natural caution about perceived risks. As a result, many compliance teams will not be using any AI, let alone GenAI. This hesitancy means these teams are missing out on significant benefits. Especially at a time when other less risk-averse industries are experiencing the upside of implementing this technology across their systems.

To avoid falling behind other diverse industries and competitors, it’s time for compliance teams to seriously consider AI. They need to understand the ways the technology – specifically GenAI – can be utilised in safe and tested ways. And without introducing any unnecessary risk. Doing so will revolutionise their internal processes, save work hours and keep budgets down accordingly.

Understanding and overcoming GenAI barriers

GenAI is a new and rapidly developing technology. Therefore, it’s natural compliance teams may have reservations surrounding how it can be applied safely. Particularly, teams tend to worry about sharing data, which may then be used in its training and become embedded into future models. Moreover, it’s also unlikely most organisations would want to share data across the internet. Strict privacy and security measures would first need to be established.

When thinking about the options for running models securely or locally, teams are likely also worried about the costs of GenAI. Much of the public discussion of the topic has focussed on the immense budget required for preparing the foundation models.

Additionally, model governance teams within organisations will worry about the black box nature of AI models. This puts a focus on the possibility for models to embed biases towards specific groups, which can be difficult to identify.

However, the good news is that there are ways to use GenAI to overcome these concerns. This can be done by choosing the right models which provide the necessary security and privacy. Fine-tuning the models within a strong statistical framework can reduce biases.

In doing so, organisations must find the right resources. Data scientists, or qualified vendors, can support them in that work, which may also be challenging.

Overcoming the challenges of compliance with AI

Despite initial hesitancy, analysts and other compliance professionals are positioned to gain massively by implementing GenAI. For example, teams in regulated industries – like banks, fintechs and large organisations – are often met with massive workloads and resource limits. Depending on which industry, teams may be held responsible for identifying a range of risks. These include sanctioned individuals and entities, adapting to new regulatory obligations and managing huge amounts of data – or all three.

The process of reviewing huge quantities of potential matches can be incredibly repetitive and prone to error. If teams make mistakes and miss risks, the potential impact for firms can be significant. Both in terms of financial and reputational consequences.

In addition, false positives – where systems or teams incorrectly flag risks and false negatives – where we miss risks that should be flagged, may come from human error and inaccurate systems. They are hugely exacerbated by challenges such as name matching, risk identification, and quantification.

As a result, organisations within the industry quite often struggle to hire and retain staff. Moreover, this leads to a serious skills shortage amongst compliance professionals. Therefore, despite initial hesitancy, analysts and other compliance professionals stand to gain massively by implementing GenAI without needing to sacrifice accuracy.

Generative AI – welcome support for compliance teams

There are numerous useful ways to implemented GenAI and improve compliance processes. The most obvious is in Suspicious Activity Report (SAR) narrative commentary. Compliance analysts must write a summary of why a specific transaction or set of transactions is deemed suitable in a SAR. Long before the arrival of ChatGPT, forward thinking compliance teams were using technology based on its ancestor technology to semi-automate the writing of narratives. It is a task that newer models excel at, particularly with human oversight.

Producing summarised data can also be useful when tackling tasks such as Politically Exposed Persons (PEP) or Adverse Media screenings. This involves compliance teams performing reviews or research on a client to check for potential negative news and data sources. These screenings allow companies to spot potential risks. It can prevent them from becoming implicated in any negative relationships or reputational damage.

By correctly deploying summary technology, analysts can review match information far more effectively and efficiently. However, like with any technological operation, it is essential to consider which tool is right for which activity. AI is no different. Combining GenAI with other machine learning (ML) and AI techniques can provide a real step change. This means blending both generalised and deductive capabilities from GenAI with highly measurable and comprehensive results available in well-known ML models.

Profiling efficiency with AI

For example, traditional AI can be used to create profiles, differentiating large quantities of organisations and individuals separating out distinct identities. The new approach moves past the historical hit and miss where analysts execute manual searches limiting results by arbitrary numeric limits.

Once these profiles are available, GenAI can help analysts to be even more efficient. The results from the latest innovations already show GenAI-powered virtual analysts can achieve, or even surpass, human accuracy across a range of measures.

Concerns about accuracy will still likely impact the rate of GenAI adoption. However, it is clear that future compliance teams will significantly benefit from these breakthroughs. This will enable significant improvements in speed, effectiveness and the ability to respond to new risks or constraints.

Ripjar is a global company of talented technologists, data scientists and analysts designing products that will change the way criminal activities are detected and prevented. Our founders are experienced technologists & leaders from the heart of the UK security and intelligence community all previously working at the British Government Communications Headquarters (GCHQ). We understand how to build products that scale, work seamlessly with the user and enhance analysis through machine learning and artificial intelligence. We believe that through this augmented analysis we can protect global companies and governments from the ever-present threat of money laundering, fraud, cyber-crime and terrorism.

  • Artificial Intelligence in FinTech
  • Cybersecurity in FinTech

The AXA Group aims to protect over 20 million customers through inclusive insurance globally by 2026

AXA Egypt and Post for Investment (PFI), the investment arm of Egypt Post, are establishing the first micro-insurance company in Egypt. This strategic collaboration is made possible by leveraging the new insurance law and aims to revolutionise the insurance landscape in the country.

Financial Inclusion

This initiative is fully aligned with AXA´s conviction that postal networks play a crucial role in global financial inclusion. Over a quarter of the world’s adult population accesses formal financial services through their post office. AXA notably signed a partnership with the Universal Postal Union (UPU) in May 2024. Moreover, this collaboration with UPU includes a research program. It will showcase successful postal insurance models and the establishment of the Postal Insurance Technical Assistance Facility (PITAF). This will promote financial inclusion and risk mitigation among underserved populations. Through this partnership, AXA is pushing the boundaries of insurance to better protect all. Solidifying its dedication to inclusive insurance practices worldwide.

The Egypt Post, who will be the main distribution channel of this JV, is a well-respected organisation. It has a strong nationwide presence, renowned for its last mile distribution capabilities and robust brand credibility. Additionally, with over 4000 branches, kiosks, and mobile trucks across all governorates, Egypt Post is an integral part of the country’s infrastructure. It caters to the population with unparalleled reach.

“We believe in the power of collaboration to create lasting change, and this joint venture is a testament to our commitment to inclusive insurance. Together, we are revolutionising the insurance landscape in Egypt to better protect and empower communities, setting new benchmarks for millions seeking reliable and accessible insurance protection.”

Garance Wattez-Richard

Micro-insurance from AXA

The product categories will include both retail and group offerings. Embedded and voluntary options will cater to diverse needs. The range of products will cover various areas. These include hospital cash, personal accident, term life, payment protection, credit life, livestock, and group protection, ensuring comprehensive coverage for the customers.

The ambitious goal is to reach 12 million customers within the first decade of operation. Therefore, underlining the commitment to making a significant impact on the lives of Egyptians through tailored insurance solutions.

This collaboration between AXA EssentiALL, AXA Egypt and PFI/Egypt Post marks a significant milestone in the local insurance industry. It paves the way for inclusive and impactful micro-insurance offerings that have the potential to transform the socio-economic landscape of Egypt. As the first of its kind, this micro-insurance company is poised to set new benchmarks. Furthermore, it can become a beacon of hope for millions of Egyptians seeking reliable and accessible insurance protection.

  • InsurTech

As businesses increasingly turn to AI to drive efficiencies in customer service operations, James Towner, Chief Growth Officer at ArvatoConnect, explores how businesses can strike the right balance of using digital technologies that empower successful human interactions.

Generative AI continues to transform how businesses engage with their customers. Buy-now-pay-later-giant Klarna is the latest to grab headlines for integrating an AI customer service chatbot that manages the equivalent workload of 700 employees. Klarna’s bosses have hailed AI as delivering superior experiences for their customers, saying its chatbot has a customer satisfaction score similar to human agents. However, studies find AI is no panacea for customer service success just yet.

AI vs the human touch

AI can undoubtedly play a major role in automating more routine queries. It provides a dynamic augmentation to the agent’s role by providing consistent, relevant information to the agent’s fingertips. But in many instances human interaction is an invaluable part of the customer experience.

In addition, customers have a variety of needs not least when it comes to those with vulnerabilities. The latest report from ArvatoConnect found how consumers that self-identify as being vulnerable said they prefer some level of human interaction when seeking help from a business. AI tools are unlikely to fully understand their unique needs.

A separate study by Smart Money People also highlights that nearly half of financial services customers (48%) are frustrated by a lack of access to human support. And an over-reliance on chatbots (24%) from firms. This epitomises the challenge facing customer services transformation projects in financial services and other categories. How can businesses get the right balance between AI and the human touch to optimise the customer experience? And what are the risks to getting the balance wrong?

Humanising the digital, digitising the human

Undoubtedly businesses can drive efficiencies in customer service operations with the help of technologies. These include AI, machine learning for analysing customer data, and robotic process automation (RPA) for handling repetitive tasks like extracting data from financial documents and using next generation chatbots. They allow human agents to focus on more complex issues, bringing empathy and creativity to their interactions.

Combining AI and human agents can then enable what we call ‘humanising the digital and digitising the human’. It represents a hybrid approach. For example, live speech AI analytics can provide helpful prompts or insights for agents, during conversations with customers, while freeing up their time.

Automating quality assurance and using generative AI to summarise customer interactions is helping to boost agents’ productivity while driving upskilling and training. Sentiment analysis and conversation analytics can also help agents to identify triggers for vulnerability. This can help them to provide the right level of support customers need and identify the next best action to take.

Developments of these customer service technologies will continue to drive transformation. Advanced tools can assess past and present customer data, suggest personalised next steps and guide agents through complex interactions. This helps ensure they deliver the right outcomes quickly and effectively to all customers.

Addressing the imbalance

Encouragingly, addressing the balance of AI and human agents is on the radar of businesses. Nearly a third (29%) of financial services businesses told us in a separate study that they planned to move the focus away from AI to human contact.

However, this compares to 51% in our study saying they planned to introduce more technology, such as AI and automation, to support the customer experience.

Understandably, many businesses see such technology as a route to saving money. But cost savings can still be reaped by empowering human agents with the right digital tools.

Companies can set clear goals for which processes need improvement, design solutions that meet those specific needs, and take a people-first approach. What this means, is using technology at the right times, in the right places – what we call ‘digital orchestration’ – and always knowing why it’s being used and what it’s expected to deliver.

Supporting vulnerable customers with AI

This is even more important when it comes to vulnerable customers, tailoring options like access to a human, to avoid the risk of alienating a large customer base

Nearly half (47%) of people in the UK identify as vulnerable, according to the Financial Conduct Authority. These individuals may face one or more of a wide range of unique challenges like mental or physical health issues, or have experienced difficult life events like bereavement.

Our study, which polled 250 individuals who self-identify as vulnerable, found that more than three-quarters (78%) of vulnerable consumers said that they prefer some level of human contact when seeking help, as many feel AI tools fail to fully understand their unique needs, leading to delays and frustration.

Nearly half (48%) of those who identify as vulnerable also admitted to avoiding businesses entirely when they do not provide adequate support tailored to their needs: largely in the form of inadequate human interaction.

However, 56% of those surveyed felt that AI and technology could meet their needs just as well as a human could. This reflects a growing acceptance of digital solutions, indicating that while many still prefer human contact, there is an openness among some vulnerable customers to engage with AI-driven assistance, as the impact of this advancing technology continues to permeate all in society.

Critically, in striking the right balance between humans and AI, businesses need to understand the preferences of their customers and how they want to interact with the organisation.

Looking ahead

Many business leaders will be turning to their IT and customer experience directors to see how they can replicate the apparent success of businesses like Klarna in adopting AI while reducing agent capacity. Yet any customer service transformation project must consider the risks of failing to balance AI and the human touch and what impact it might have on customers.

Businesses have the most to gain by using technology in a way that supports and enhances the human experience, for both the agents and the customer – creating personalised and genuine interactions that solve customer issues in the shortest amount of time.

James Towner, Chief Growth Officer, ArvatoConnect

  • Artificial Intelligence in FinTech

FinTech Strategy spoke with Ryan O’Holleran, Head of Sales, Enterprise, EMEA at Airwallex, to learn about the global payments and financial infrastructure provider

Airwallex, a financial infrastructure provider, offers a range of services. Including multicurrency accounts, payment acceptance card issuing, foreign exchange (FX) payouts, treasury and expense management. In addition to supporting small and medium-sized businesses, the company also provides APIs and a software layer for direct access to enterprise businesses. As well as an enterprise platform product called Scale. Airwallex has found success working across various industries. It works with the likes of Bird (formerly MessageBird) to handle global accounts and backend treasury, and partners with Qantas to offer financial tools for their business partners.

The company also enables faster and more efficient payments through its patchwork network of financial partnerships and licenses. Airwallex has experienced significant growth even during economic downturns. As of August this year, Airwallex globally processed transactions worth more than $100 billion annually and saw a 73 percent year-on-year increase. It is now focused on embedded finance solutions and global expansion.

At Money20/20 Europe, FinTech Strategy spoke with Airwallex’s Head of Sales, Enterprise, EMEA, Ryan O’Holleran, to find out more…

Tell us about the genesis of Airwallex?

“Our co-founder, Jack Zhang, started a coffee company in Melbourne, Australia, which is still around today, with a few friends from university. And while they were building out this coffee shop, they were buying beans from abroad, along with supplies and packaging. They found how hard it was to actually pay for services, send funds abroad and deal with multiple currencies. So, they saw an opportunity to help streamline the financial infrastructure for small businesses. That’s when Jack and his co-founders put Airwallex together and built out an initial SME’s use case to allow multicurrency accounts and FX payouts. Since then, the business has really expanded…

Today, Airwallex provides a set of APIs – we’re really providing financial infrastructure to move money globally. On those APIs, we also have a layer of software that we can offer direct access to enterprise businesses. The third part of this, which is kind of the new product over the last three years, is our enterprise platform product called Scale. Scale allows you to embed those financial services into a product as well as a platform or marketplace. So, you kind of think about it as a direct treasury product, APIs and a platform product.”

Tell us about your role at Airwallex?

“I’m originally from San Francisco, grew up in the Bay area, started in tech, did a couple of startups, and I actually got into payments via Stripe. I joined Stripe back when there were about 200 employees in San Francisco. Spent some time in Chicago and then moved to the UK initially with Stripe. I was there for about five and a half years, as we went from 200 staff to 6,000. At that point, I wanted to get back to something a little bit different. To help more cross-functioning with product and help scale businesses. The opportunity with Airwallex came along where I saw the company addressing many things my customers at Stripe were asking for.

So, the FX piece, mass payouts, treasury, all complimented what Stripe is doing with acquiring. Since I joined the team three years ago, we’ve been scaling across EMEA. We now have offices in London, Amsterdam, Vilnius and just last year launched our office in Tel Aviv to cover Israel. And we have teams in the Americas and APAC where Airwallex was founded.”

What are some of the key challenges financial institutions are facing that you can help them with? What problems are companies asking you to solve? In doing so, what are the challenges for Airwallex?

“We work in different areas. This is where I think we have differentiated the business and also where I see the industry moving. If you look back over the last five, 10 years, there was this approach where you had Stripe and all the major players coming in and saying, we can do things and we can do it really well and you only need to use us, you don’t need to use a patchwork of providers. I think that is starting to shift. You see this with orchestration layers like Primer or Gravy, allowing people to be agnostic on PSPs. And then you’re seeing people think about redundancy. So, the heads of payments we’re talking to this week are looking at two or three providers because they need redundancy or want to use the best provider in each region. They don’t want to be siloed.

Airwallex can be used in a segmented approach. So, if you just need us for payouts, you can do that. If you just need us for FX, you can do that. If you just need us for acquiring, you can do that. Or we could do that globally and you can adjust as you see fit. So, the flexibility of Airwallex I think is one of our superpowers.”

Tell us about some of the successful partnerships Airwallex has been involved in…

“The interesting thing about Airwallex is that since we’re providing financial infrastructure, there’s a huge variety of customers we work with. One of the local ones is Bird (a cloud communications platform that connects enterprises to their global customers). Using our software product they are creating global accounts, handling backend treasury, payroll, suppliers and more. We’ve also worked with Qantas to build out an SMB solution embedding all of the Airwallex financial services and they call it Qantas Business Money.          

Elsewhere, Brex in the US were looking for a provider to help with their payout rails. One of the things Airwallex has done is rebuilt the Swift network via local rails. So, we have a patchwork network of financial partnerships and licences where if you are located, let’s say in the US, but you want to pay somebody out in the UK, you get access to faster payment rails having never set foot in the UK or separate rails via Europe having never set foot in the EU. So, you get this mass payoff solution of local rails, which is faster, cheaper, and more efficient than using something like Swift.”

“I think where we’re seeing a lot of opportunities, in EMEA specifically, in B2B, vertical, SaaS, travel and marketplaces, is this embedded finance solution. It was kind of a buzzword a few years ago and now we’re actually starting to see it develop. I view it as actually embedding all of these financial services – whether it be a wallet, issued cards, or local multi-currency accounts – and being able to monetize that. So, we’re seeing this with a lot of our customers actually wanting to white label our products, embed that and bring payments on platform.”

And what’s next for Airwallex? What future launches and initiatives are you particularly excited about?

“The growth of Airwallex, specifically on a global scale, over the last few years is one thing I’m very proud of because it’s happened during one of the worst economic downturns we’ve experienced. FinTech was almost retracting in terms of budgets and investments. You’re starting to see the tide turn, but we were able to grow over 100 percent year on year, through some of the toughest times for business. And now we’re really starting to see that pick up because the businesses, who actually decided this is going to be a building year for us now, they’re going live, they’re accelerating, they’re growing.

And so we’re seeing the ROI of that investment. It’s a testament to the global financial infrastructure we’ve built. Meanwhile, Airwallex became cash flow positive in 2023. It now processes more than $100 billion in annualised transaction volume. The company now employs over 1,500 people worldwide working across 23 international offices.”

Why Money20/20? What is it about this particular event that makes it the perfect place to showcase what you do? How has the response been to Airwallex?

“The great thing about Money20/20, here in Europe, and in Asia and the US, is the good division between buyers and sellers. So, you have all these service providers like Airwallex, Amex, etc… And then you have the Heads of Payments from companies like Booking.com, Vinted and SumUp who are coming here with their teams to meet with providers. If you think about that from a sales perspective, those meetings are very hard to get outside of this environment. But over a week you get 15 different meetings each day that would normally take months to arrange. So, the ROI from this week is really powerful just from being able to have these conversations. Three years ago, we first came to suss out the event and as we’ve grown the response has grown. People are being proactive and keen to engage with us which is exciting to see.”

  • Digital Payments
  • Embedded Finance

Hugo Farinha, Co-founder and CTO at Virtuoso QA on why AI is driving organisational change across financial services

We’ve seen an enormous amount of discussion concerning all aspects of AI since the emergence of Chat GPT made it headline news. However, most articles and conversations focusing on its business impact seem to wilfully ignore the ‘elephant in the room’. Namely, the inevitable organisational change AI will usher in, especially for employees.

AI technology driving change

To ignore change is folly, and likely to have the exact opposite effect that businesses and AI technology vendors want. We can’t pretend workforces won’t be disrupted by such a seismic technological advance. Certain job roles will become obsolete. Business leaders can’t run the risk of creating a culture of fear and uncertainty among employees who are unlikely to be fooled.

It’s true AI could lead to leaner operations, particularly in insurance and finance companies, with fewer employees needed for routine tasks, but only half the story. Smart businesses will almost certainly reinvest cost savings into new growth areas that require specific human talent. Companies that maintain a strong human element in customer service and personalised offerings will differentiate themselves in a crowded market. The rise in AI-driven, agile companies will create faster market shifts and greater competition.

While AI has the potential for productivity and efficiency gains, and even to do the same with less if needed, I actually don’t predict major job culls in the next few years. AI is particularly good at data processing and data analytics, in insurance for example. So, when more data can be processed and analysed, human intervention can make better informed decisions as a result. In the short to medium term, data analysis and decision making will remain firmly in the human realm. But powered by AI.

The Future for Artificial Intelligence

Meanwhile, the technology is still evolving, and organisations need to build a model that layers over the top of AI – powered by it, rather than replaced by it. Despite the hype, we are still a long way from AI becoming an entity that can lead, implement and operate itself to a purposeful end. But it will increasingly power applications overlaid by strategic, human-led frameworks.

To achieve this, leaders must bring their teams with them on the journey. In the field of testing for example, developers have traditionally written code as part of their role. This is a very time consuming and laborious task. Historically skills gaps have led to delays in progress. But the ability to ‘outsource’ to AI has freed up the time of those developers to focus on the purpose of that code in relation to the product. And, ultimately, the customer. Similarly, leaders in all fields need a broader understanding of AI use cases such as these to make effective strategic decisions. For example, on hiring. Understanding when to bring in more people and when to bring in new technology to complement the skills of your existing team means understanding AI’s strategic implications, technical capabilities and limitations.

An Evolving Job Market

From the perspective of the employee, the job market will continuously evolve alongside AI advancements. It will require ongoing adaptation and learning to stay relevant. Skills such as empathy, communication, and negotiation will remain vital. These are differentiators and difficult for AI to replicate. Understanding AI tools and data analysis will be increasingly important, even for non-technical roles. The ability to adapt to new technologies and continuously learn will be essential. Moreover, as AI becomes more integrated, the need for professionals who understand the ethical implications and regulatory requirements will grow exponentially.

Driving growth and job creation in this new world will require a different mindset to the current received wisdom. From both employees and leaders. In addition to the advances and changes already discussed, AI also has the potential to level the playing field, enabling smaller or newer companies to compete more effectively with, and even seriously threaten, established players. With many traditional barriers to entry such as burdensome start-up costs removed, new business models are likely to emerge. In much the same way as they did in the early days of the internet. Investors will be on the lookout for the next ‘giant killer’.

This will create opportunities for those with the foresight to upskill, as well as for those looking to start their careers. Although those opportunities and the jobs of tomorrow may not yet be completely clear. What is clear, however, is that established businesses cannot afford to be complacent. Change is inevitable and empires can be toppled overnight by technology as disruptive as AI. By embracing it early, leaders in those businesses will have the opportunity to spot and fix the gaps and redundancies in their business models that the technology and its capabilities exposes before the market does so more painfully and publicly.

Our mission is to enable and lead the world’s quality-first revolution. QA tools haven’t kept up with the demands of the testing world. Virtuoso is here to deliver with AI-powered, low-code/no-code test automation to support the modern business.

“Virtuoso technology represents the foundation for software quality in the digital world, and we are proud to be a critical, guiding force in the era of AI.”

Darren Nisbet, CEO, Virtuoso

  • Artificial Intelligence in FinTech

Cullen Zandstra, CTO at FloQast on mitigating the risks of AI to deliver benefits to financial services

There’s a lot of buzz around Generative AI (GenAI). What’s not always heard beneath the noise are the very real and serious risks of this fast-developing AI tech. Let alone ways to mitigate these emerging threats.

Currently, one quarter (26%) of accounting and bookkeeping practices in the UK have now adopted GenAI in some capacity. That figure is predicted to grow for many years to come.

With this in mind, and as we hit the crest of the GenAI hype cycle, it’s critically important that leaders focus closely on the potential risks of AI deployment. They need to proactively prepare to mitigate them, rather than picking up the pieces after an incident.

Navigating the risky transition to AI

The benefits of AI are well-proven. For finance teams, AI is a powerup that unlocks major performance and efficiency boosts. It significantly enhances their ability to generate actionable insights swiftly and accurately, facilitating faster decision-making. AI isn’t here to take over but to augment the employees’ capabilities. Ultimately improving leaders’ trust in the reliability of financial reporting.

One of the most exciting aspects of AI is its potential to enable organisations to do more with less. Which, in the context of an ongoing talent shortage in accounting, is what all finance leaders are seeking to do right now. By automating routine tasks, AI empowers accountants to focus on higher-level analysis and strategic initiative, whilst drawing on fewer resources. GenAI models can help to perform routine, but important tasks. These include producing reports for key stakeholders and ensuring critical information is effectively and quickly communicated. It enables timely and precise access to business information, helping leaders to make better decisions.

However, GenAI also represents a new source of risk that is not always well understood. We know that threat actors are using GenAI to produce exploits and malware. Simultaneously levelling up their capabilities and lowering the barrier of entry for lower-skilled hackers. The GenAI models that power chatbots are vulnerable to a growing range of threats. These include prompt injection attacks, which trick AI into handing over sensitive data or generating malicious outputs.

Unfortunately, it’s not just the bad guys who can do damage to (and with) AI models. With great productivity comes great responsibility. Even an ambitious, forward-thinking, and well-meaning finance team could innocently deploy the technology. They could inadvertently make mistakes that cause major damage to their organisation. Poorly managed AI tools can expose sensitive company and customer financial data, increasing the risk of data breaches.

De-risking AI implementation

There is no technical solution you can buy to eliminate doubt and achieve 100% trust in sources of data with one press of a button. Neither is there a prompt you can enter into a large language model (LLM).

The integrity, accuracy, and availability of financial data are of paramount importance during the close and other core accountancy processes. Hallucinations (another word for “mistakes”) cannot be tolerated. Tech can solve some of the challenges around data needed to eliminate hallucinations – but we’ll always need humans in the loop.

True human oversight is required to make sure AI systems are making the right decisions. We must balance effectiveness with an ethical approach. As a result, the judgment of skilled employees is irreplaceable and is likely to remain so for the foreseeable future. Unless there is a sudden, unpredicted quantum leap in the power of AI models. It’s crucial that AI complements our work, enhancing rather than compromising the trust in financial reporting.

A new era of collaboration

As finance teams enhance their operations with AI, they will need to reach across their organisations to forge new connections and collaborate closely with security teams. Traditionally viewed as number-crunchers, accountants are now poised to drive strategic value by integrating advanced technologies securely. The accelerating adoption of GenAI is an opportunity to forge links between departments which may not always have worked closely together in the past.

By fostering a collaborative environment between finance and security teams, businesses can develop robust AI solutions. They can boost efficiency and deliver strategic benefits while safeguarding against potential threats. This partnership is essential for creating a secure foundation for growth.

AI in accountancy: The road forward

The accounting profession stands on the threshold of an era of AI-driven growth. Professionals who embrace and understand this technology will find themselves indispensable.

However, as we incorporate AI into our workflows, it is crucial to ensure GenAI is implemented safely and does not introduce security risks. By establishing robust safeguards and adhering to best practices in AI deployment, we can protect sensitive financial information and uphold the integrity of our profession. Embracing AI responsibly ensures we harness its full potential while guarding against vulnerabilities, leading our organisations confidently into the future.

Founded in 2013, FloQast is the leading cloud-based accounting transformation platform created by accountants, for accountants. FloQast brings AI and automation innovation into everyday accounting workflows, empowering accountants to work better together and perform their tasks with greater efficiency and accuracy. Now controllers and accountants can spend more time delivering greater strategic value while enjoying a better work-life balance.

  • Artificial Intelligence in FinTech
  • Cybersecurity in FinTech

Russ Rawlings, RVP, Enterprise, UK&I at Databricks, on the future of AI in FinTech

Strict regulation, along with time and cost restraints, means financial services must take a measured approach to technological advancements. However, with the emergence of GenAI, particularly large language models (LLMs), organisations have an opportunity to maximise the value of their data to streamline internal operations and enhance efficiencies. 

Embracing GenAI has never been more important for organisations looking to stay ahead of the curve. 40-60% of the global workforce will be impacted by the growth of AI. Moreover, global adoption of GenAI could add the equivalent of $2.6tn to $4.4tn in value annually to global industries. The banking sector stands to gain between $200-340 billion.

But whilst the financial services industry can gain incredible benefits from GenAI, adoption is not without its challenges. Financial organisations must prioritise responsible data management. They must also navigate strict privacy regulations and carefully curate the information they use to train their models. But, for companies that persevere through these obstacles, the benefits will be substantial. 

Building customised LLMs for financial services 

Consumer chatbots have brought GenAI to the mainstream. Meanwhile, the true potential of this transformative technology lies in its ability to be tailored to the unique needs of any organisation, in any industry. Including the financial sector. 

Risk assessment, fraud prevention, and delivering personalised customer experiences are some of the use cases of custom open source models. Created using a company’s proprietary data, these models ensure relevant and accurate results. And are more cost-effective due to their smaller datasets. For instance, banks can use a customised model to seamlessly analyse customer behaviour and flag up any suspicious or fraudulent activities. Or, a model can leverage sophisticated algorithms to assess an individual’s eligibility for a loan.

Another huge benefit of these tailored systems is trust and security. Deploying a custom open-source model eliminates the need to share sensitive information with third parties. This is crucial for organisations operating within such a highly regulated industry. This approach also democratises the training of custom models. Furthermore, it allows organisations to harness the power of GenAI whilst retaining control and compliance.

Using data intelligence to boost AI’s impact

To truly harness the power of GenAI, organisations must cultivate a deep understanding of data across the entire workforce. Every employee, regardless of how technical they are, must grasp the importance of proper data storage. Also how data can be used to improve decision-making.

Organisations can use a data intelligence platform to help implement this. Built on a lakehouse architecture, a data intelligence platform provides an open, unified foundation for all data and governance. It operates as a secure end-to-end solution tailored to the specific needs of the financial services industry. By adopting such a platform, businesses can eliminate their reliance on third party solutions for data analysis. They can create a streamlined approach to data governance and accelerate data-driven outcomes. Users across all levels of the business can navigate their organisation’s data, using GenAI to uncover important insights.

The future of AI in the financial sector

The path to success lies in embracing GenAI as a canvas for crafting bespoke solutions. Whilst no two financial institutions are exactly the same, the industry’s tools must strike a delicate balance between supporting specific use cases and addressing broader requirements, Customised, open source LLMs and data intelligence platforms hold the key, sparking transformative change across the sector. These tailored solutions will empower financial businesses to integrate cutting-edge innovations and ensure  security, governance and customer satisfaction. Organisations that embrace this change will not only gain a competitive edge, but also pave the way for larger transformations, re-shaping the financial landscape and setting new standards for the industry.

Databricks is the data and AI company with origins in academia and the open source community. Databricks was founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow. As the world’s first and only lakehouse platform in the cloud, Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI.

  • Artificial Intelligence in FinTech

Pat Bermingham, CEO of B2B digital payment specialist Adflex, asks what impact will Artificial Intelligence really have on B2B payments?

Visit any social media newsfeed and countless posts will tell you AI means “nothing will ever be the same again”. Or even that “you’re doing AI wrong”. The volume of hyperbolic opinions being pushed makes it almost impossible for businesses to decipher between hype and reality.

This is an issue the European Union’s ‘AI Act’ (the Act), which came into force on 1 August 2024, aims to address. The Act is the world’s first regulation on artificial intelligence. It sets out how to govern the deployment and use of AI systems. The Act recognises the transformative potential AI can have for financial services, while also acknowledging its limitations and risks.

Within the debate about AI in financial services, B2B payments are an area where AI has huge potential to accelerate digital innovation. Let’s go beyond the hype to provide a true perspective on what AI really means for B2B payments specifically.

Understanding what AI is, and what it isn’t

AI is a system or systems that can perform tasks that normally require human intelligence. It incorporates machine learning (ML). ML has been used by developers for years to give computers the ability to learn without being explicitly programmed. In other words, the system can look at data and analyse it to refine functions and outcomes.

A newer part of this is ‘deep learning’, which leverages multi-layered neural networks. This simulates the complex decision-making power of our brains. The deep learning benefits outlined later in this article are based on Large Language Models (LLMs). LLMs are pre-trained on representative data (such as payment/transaction/tender data). Deep learning AI does not just look at and learn patterns of behaviour from the data. It is becoming capable of making informed decisions based on this data.

Before we explore what this means for B2B payments, let’s make one caveat clear: human supervision is still needed to ensure the smooth running of operations. AI is a supporting tool, not a single answer to every question. The technology is still maturing. You cannot hand over the keys to your B2B payments process quite yet. Manual processes will retain their place in B2B payments. AI tools will help you learn, adapt and improve more quickly and at scale.

The AI Act – what you need to know

The Act attempts to categorise different AI systems based on potential impact and risk. The two key risk categories include:

  1. Unacceptable risk – AI systems deemed a threat to people, which will be banned. This includes systems involved in cognitive behavioural manipulation, social scoring, and real-time biometric identification.
  2. High risk – AI systems that negatively affect safety or fundamental rights. High-risk AI systems will undergo rigorous assessment and must adhere to stringent regulatory standards before being put on the market. These high risk systems will be divided into two further categories:
  3. AI systems that are used in products falling under the EU’s product safety legislation, including toys, aviation, cars, medical devices and lifts.
  4. AI systems falling into specific areas that will have to be registered in an EU database.

The most widely used form of AI currently, ‘generative AI’ (think ChatGPT, Copilot and Gemini), won’t be classified as high-risk. However, it will have to comply with transparency requirements and EU copyright law.

High-impact general-purpose AI models that might pose systemic risk, such as GPT-4o, will have to undergo thorough evaluations. Any serious incidents would have to be reported to the European Commission.

The Act aims to become fully applicable by May 2026. Following consultations, amendments and the creation of ‘oversight agencies’ in each EU member state. Though, as early as November 2024, the EU will start banning ‘unacceptable risk’ AI systems. And by February 2025 the ‘codes of practice’ will be applied. 

So, with the Act in mind, how can AI be used in a risk-free manner to optimise B2B payments?

AI will transform payment data analysis

Today’s B2B payment platforms are not one-size-fits-all solutions; instead, they provide a toolkit for businesses to customise their payment interactions.

AI-based LLMs and ML can be used by payment providers to rapidly understand and interpret the extensive data they have access to (such as invoices or receipts). By doing this, we gain insights into trends, buyer behaviour, risk analysis and anomaly detection. Without AI, this is a manual, time consuming task.

One tangible benefit of this data analysis for businesses comes from combining payment data with knowledge of a wide range of vendors’ skills, products and/or services. AI could then, for example, identify when an existing supplier is able to supply something currently being sourced elsewhere. By using one supplier for both products/services, the business saves through economies of scale.

Another benefit of data analysis comes from payment technology experts. Ours have been training one service to extract data from a purchase order or invoice, to flow level 3 data, which is tax evident in some territories. This automatically provides the buyer with more details of the transaction, including relevant tax information, invoice number, cost centre, and a breakdown of the products or service supplied. This makes it easy and straightforward to manage tax reporting and remittance, purchase control and reconciliation.

AI-driven data analysis isn’t just a time and money-saver, however. It also adds new value by enabling providers to use the data to create hyper-personalised payment experiences for each buyer or supplier. For example, AI and ML tools could look out for buying and selling opportunities, and perform a ‘matchmaking supplier enablement service’ that recommends the best payment methods – and the best rates – for different accounts or transactions. The more personalised a payment experience is, the happier the buyer and more likely they are to (re)purchase.

Efficient data flows mean stronger cash flows

Another practical application of AI is to help optimise cash management for buyers. This is done by using the data to determine who is strategically important and when to pay them. It could even recommend grouping certain invoices together for the same supplier, consolidating them into one payment per supplier, reducing interchange fees and driving down the cost of card acceptance.

AI can also perform predictive analysis for cash flow management, rapidly analysing historical payment data to predict cash flow trends, allowing businesses to anticipate and address potential challenges proactively. This is particularly valuable in the current economic climate where cashflow is utterly vital.

By extracting value-added, tax evident data from a purchase order or invoice, AI can rapidly analyse invoices and receipts to enable efficient, accurate automation of the VAT reclaims process. Imagine: the time comes for your finance team to reclaim VAT on recent invoices and receipts, but they don’t have to manually go through every receipt or invoices and categorise them into a reclaim pile or not reclaimable. It sounds like a dream but it will be the reality for business everywhere: AI does the heavy lifting and humans verify it, saving significant time and resources.

Quicker, more accurate invoice reconciliation

The third significant benefit of AI is automated invoice reconciliation. By identifying key information from an invoice and recognising regular payees, AI can streamline and automate the review process. This has the potential to significantly speed up transactions and enable more efficient payment orchestration.

Binding together all supporting paperwork, such as shipping, customs, routes, and JIT (just-in-time) requirements can also be done by AI, and it’s likely to be less prone to human error.

This provides an amazing opportunity to make B2B payments faster, reduce costs and increase efficiency.  Businesses know this: 44% of mid-sized firms anticipate cost savings and enhanced cash flow as a direct result of implementing further automation within the next three years. According to American Express, 48% of mid-sized firms expect to see payment processes accelerate, with more reliable payments and a broader range of payment options emerging.

When. Not if.

There are significant opportunities to leverage AI in B2B payment processes, making it do the heavy lifting. It is, however, essential to view these opportunities with a balanced understanding of the limitations of AI.

While all the opportunities for AI in B2B payments outlined here are based on relatively low-risk AI systems, human oversight of these systems is still essential. However, with all the freed-up time and resource achieved through the implementation of AI, this issue can be avoided.

AI in B2B payments is not an if, but a when. The question is, when will you make the jump, hand in hand with technology, rather than fearing it or passing full control over to it.

In order to grow, it is essential for users to see the tangible benefits. For example, by enhancing efficiencies in account payable (AP), businesses can reallocate time and resource previously spent in AP to other areas. Early adopters are starting to test the water but only time will tell how much of an impact AI will make.

Most businesses will likely wait for the early adopters to fail, learn and progress. If something goes wrong in B2B payments, it can have a huge impact on individuals, businesses and economises. Only when the risk is clearly defined and manageable will AI truly become the gamechanger in B2B payments that all the hype claims.

Adflex has been at the heart of the B2B fintech revolution from the beginning. We are known for fostering innovation and helping companies harness the power of digital payments. Our technology and expertise bring together buyers and suppliers to make transactions fast, cost-effective and straightforward to manage. We take the pain out of the supply chain by delivering seamless and secure payment integration that adds value to both buyers and merchants.”

  • Artificial Intelligence in FinTech
  • Digital Payments

Michael Donnelly, Head of Client Success at BlueFlame AI, on how to prepare your firm to attract and retain the next generation of AI talent

In the fast-paced world of financial services, a new generation is stepping in with high expectations for generative artificial intelligence (AI) in the workplace. Recently, BlueFlame AI conducted a specialised training session for one of our private equity clients, aimed at their newly hired summer intern class. The experience was eye-opening for us. Furthermore, it also provided a great lesson in the growing importance of AI in the industry and the expectations today’s young professionals have as they enter the workforce

AI & LLMs

The comprehensive training session covered vital areas such as AI and Large Language Models (LLMs), a review of the most popular use cases the industry has adopted, and hands-on practical training in prompt engineering. Moreover, our goal was to show this next generation the skills they’ll need to leverage these tools effectively. New roles could revolutionise alternative investment management processes like due diligence, market analysis, and portfolio management.

We also used this as an opportunity to survey the group about their experience of and expectations for AI use in the workplace – and it yielded some striking insights. A significant 50% of the interns reported using ChatGPT daily, with 83% utilising it at least weekly. Furthermore, these numbers suggest young professionals expect these tools to be available to them in their professional lives. In the same way they are available in their personal lives and set to become as commonplace as traditional software in the workplace. The interns’ expectations regarding AI’s impact on their work efficiency are even more telling. An overwhelming 94% believe these tools will enhance their productivity, indicating strong faith in the technology’s potential to streamline tasks and boost performance.

These high expectations have key implications for employers. A significant 89% of interns expect their employers to provide enterprise-grade AI/LLM access. This statistic is a wake-up call for companies that have yet to invest in AI technologies, highlighting the need to stay competitive not just in terms of products and services but also in workplace technology provision.

Talent Acquisition & Retention

Perhaps most important is AI’s potential impact on talent acquisition and retention. One-third (33%) of interns surveyed indicated they would reconsider their choice of employer if they didn’t offer access to enterprise-grade AI/LLM tools. A response that could throw a serious wrench into any Financial Services firm’s hiring plans.

The message is clear for businesses looking to stay ahead of the curve when it comes to supporting their employees. Investing in AI technologies and training is no longer optional. Firms must be ready to meet the expectations of the incoming workforce. They need to provide them with the best technology to maintain a competitive edge in an increasingly AI-driven business landscape. Companies that embrace AI and provide their employees with the tools and training to harness its power will likely see significant productivity, innovation, and talent retention advantages.

AI Revolution

Private and public investment firms stand to benefit greatly from this AI revolution. As this new generation brings its enthusiasm and expectations for technology tools into the workplace, firms that are prepared to meet these expectations will be better positioned to tap into fresh perspectives, drive innovation and reap significant efficiency and productivity gains. And if firms can take a proactive approach to training and commit to developing a forward-thinking, AI-enabled workforce, they will be able to enhance their teams’ capabilities and shape the future of work in the financial sector.

Generative AI and the workplace expectations it has created mark a new paradigm in the market. The next generation of professionals is not just ready for AI – they’re demanding it. Firms that recognize and act on this trend will be well-positioned to lead the pack when it comes to innovation, efficiency and talent acquisition.

Founded in 2023 BlueFlame AI is the only AI-native, purpose built, LLM-agnostic solution for Alternative Investment Managers.

  • Artificial Intelligence in FinTech

Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer…

Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer support, and automate processes, making banks more efficient and customer-focused.

Research by McKinsey shows that over 20 percent of an organisation’s digital budget goes towards AI. The study links significant investments in AI to a 10-20 percent increase in sales. AI will play a central role in boosting efficiency, customer service, and overall banking productivity.

Introduction to AI in Personalised Banking

Delivering personalised experiences is crucial for customer satisfaction and retention. AI helps banks achieve this by collecting and analysing customer data. This data is then used to create recommendations, product offerings, and even financial advice tailored to each customer’s needs.

AI tools can optimise workflows through a technique called prescriptive personalisation, using past data to predict future behaviour. Real-time personalisation takes this further, incorporating current information alongside historical data. 

This allows banks to deliver highly customised virtual assistants and real-time recommendations powered by natural language processing (NLP) models. These AI-powered assistants not only build trust and user engagement but also simplify interactions with the bank.

Tool 1: Predictive Analytics

Predictive analytics, powered by AI tools, unlock a new level of customer personalisation in banking. These tools analyse data to uncover hidden patterns and trends that traditional methods might miss. This knowledge reveals sales opportunities, possibilities for cross-selling, and ways to improve efficiency.

Predictive analytics use past data to forecast customer behaviour and market trends. This foresight allows banks to tailor marketing strategies and sales approaches to meet changing customer needs and capitalise on emerging opportunities.

Tool 2: Chatbots and Virtual Assistants

One key advantage of chatbots is their constant availability. This is especially helpful for customers who need assistance outside of regular operating hours.

AI chatbots learn from every interaction, improving their ability to understand and meet individual customer needs. By integrating chatbots into banking apps, banks can provide personalised banking experiences and recommend financial products and services that fit a customer’s specific situation.

Erica, a virtual assistant developed by Bank of America, handles tasks like managing credit card debt and updating security information. With over 50 million requests handled in 2019 alone, Erica demonstrates the potential of chatbots as efficient assistants for customers.

Tool 3: Recommendation Engines

Banks use AI tools to analyse vast amounts of customer data, including purchases, browsing habits, and background information. This deep understanding helps banks recommend products that truly fit each customer’s needs.

These personalised recommendations extend beyond credit card suggestions. AI can identify potential investments or loans that align with a customer’s financial goals. By providing customers with relevant information, banks allow them to make informed financial decisions. 

Tool 4: Sentiment Analysis with AI

AI sentiment analysis translates written text into valuable insights. AI uses NLP to understand emotions and opinions in written communication. By examining things like customer feedback, emails, and social media conversations, banks gain a much clearer picture of customer sentiment.

Tool 5: Voice Recognition

AI-powered voice assistants offer a convenient way to handle everyday banking tasks. From checking balances to paying bills, all a customer needs are simple voice commands.

These assistants use NLP to understand customer requests and respond accurately. Voice authentication adds another layer of security by verifying customer identity during transactions.

Tool 6: Process Automation

Robotic Process Automation (RPA) automates repetitive tasks, boosting operational efficiency. It tackles up to 80 percent of routine work and frees up workers for more valuable tasks requiring human judgement.

RPA bots can handle tasks like issuing and scheduling invoices, reviewing payments, securing billing, and streamlining collections – all at once. NLP empowers these bots to extract information from documents, simplifying application processing and decision-making. 

Tool 7: Facial Recognition with AI

Facial recognition helps banks verify customer identities during tasks like opening accounts, accessing information, and making transactions. Compared to traditional passwords, facial recognition offers stronger security and greater convenience. It eliminates the need for remembering complex passwords or worrying about stolen credentials, making banking interactions smoother and less error-prone. This technology also helps prevent fraud by identifying attempts to impersonate real customers.

Capital One AI Case Study

Capital One demonstrates how AI can personalise banking. Their AI assistant uses NLP to understand customer questions and provide immediate answers. Capital One also incorporates AI into fraud detection. Machine learning and predictive analytics help pinpoint suspicious credit card activity to strengthen security measures.

Conclusion

AI tools offer a significant opportunity for banks to improve customer experiences and achieve long-term success. By personalising banking services with AI, banks can better meet individual customer needs. This leads to higher satisfaction and loyalty, which enhances the bank/customer relationship.

AI has the potential for an even greater impact. As banks integrate more advanced AI capabilities, they can create even more engaging and personalised interactions. This focus on ‘hyper-personalisation’ could be the next big step for financial institutions to set them apart in a competitive market.

  • Artificial Intelligence in FinTech

Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects…

Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects that 75 percent of financial institutions will invest $31 billion in integrating AI into their existing systems by 2025. The trend is driven by customer demand for faster and more convenient banking options.

AI excels at analysing enormous amounts of data. This lets banks find patterns and trends to personalise customer service and boost efficiency. For example, AI-powered chatbots offer 24/7 help with basic questions, freeing up customer service staff for trickier issues. AI can also analyse customer behaviour to predict their needs and suggest relevant services or support, from personalised investment options to flagging suspicious account activity.

Benefit 1: Increased Efficiency

Long wait times and impersonal interactions often leave customers frustrated with traditional bank customer service. Fortunately, AI streamlines the experience by providing quick and accurate answers. It eliminates the need to navigate complex phone menus.

AI personalises interactions and saves customers from endless button-pressing and long hold times. AI in customer service can also analyse vast amounts of customer data. The data helps banks anticipate customer needs and recommend tailored solutions, preventing problems before they arise. This results in higher customer satisfaction and a smoother banking experience.

Benefit 2: Personalisation

AI can analyse vast amounts of customer data, including purchases and browsing habits, to create detailed customer profiles. These profiles help banks recommend relevant products and services that fit individual needs.

For instance, a customer who often pays bills online might be recommended a new budgeting tool. Similarly, someone who regularly saves for travel could receive information about travel insurance or currency exchange. These personalised suggestions can come through various channels, like the bank’s website, email alerts, or chatbots.

Benefit 3: Cost Savings

Cost savings are a major advantage of AI-powered customer service in banking. One key way AI achieves this is through automation. Chatbots powered by AI can handle many routine customer inquiries, freeing up human agents for complex issues. This reduces labour costs while also improving response times.

AI also helps with better staffing management. It can analyse past data to predict how many calls are coming in. Banks can then ensure they have the right number of agents available, avoiding overstaffing or understaffing that can significantly impact costs.

Benefit 4: 24/7 Support

Traditionally, reaching a support agent often meant waiting on hold during peak hours. However, AI in customer service is transforming the industry by offering immediate assistance through chatbots. These virtual assistants provide instant support the moment a customer reaches out.

Unlike human agents with limited working hours, chatbots are available 24/7. This ensures customers get help whenever they need it, regardless of location or time zone. This is especially valuable in the globalised world, where customers might need support outside of regular business hours.

A great example of this success is Photobucket, a media hosting service. After implementing a chatbot, they offered 24/7 support to international customers. This results in a three percent increase in customer satisfaction scores along with a 17 percent improvement in resolving issues on the first try.

Benefit 5: Multilingual Support

AI-powered chatbots offer multilingual support, breaking down language barriers and creating a positive banking experience. These chatbots can figure out a customer’s preferred language at the start of a conversation. This ensures clear communication, no matter what language the customer speaks.

Conclusion

A study by Global Market Insights predicts the conversational AI market will reach $57.2 billion by 2032. This technology is making big strides in banking, particularly by automating routine tasks and inquiries. By taking care of these repetitive tasks, AI frees up human agents to focus on more complex customer issues. This improves efficiency and helps banks manage their operating costs. A streamlined customer service experience builds trust and loyalty, which can lead to business growth for financial institutions.

  • Artificial Intelligence in FinTech

The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment,…

The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment, many are embracing artificial intelligence (AI) for assistance with investment decisions. AI acts as a powerful tool, improving efficiency and effectiveness across various aspects of asset management.

From analysing market trends to building diversified portfolios, AI’s strength lies in processing massive amounts of data. Furthermore, it uncovers hidden patterns empowering managers to make data-driven investment choices across financial services.

Introduction to AI in Asset Management

Asset management involves managing investment portfolios for individuals, institutions, and businesses. This includes stocks, bonds, real estate, and other financial assets. The main goal is to grow value over time while minimising risk and meeting client goals.

AI is transforming asset management with its data processing and analytics capabilities. Additionally, AI algorithms can quickly analyse massive amounts of financial data, market trends, and economic indicators. This helps uncover hidden patterns and connections that human analysts might miss. A data-driven approach empowers asset managers to make better investment decisions and develop more accurate market forecasts.

Portfolio Management

AI is transforming asset management by offering powerful tools for better decision-making. Moreover, machine learning (ML), AI analyses vast amounts of historical market data to identify patterns and predict future trends, providing valuable insights for building portfolios.

Natural language processing (NLP) lets computers understand human language. NLP can unlock information from unstructured sources like news articles, social media, and analyst reports. The algorithms then analyse sentiment and extract key information that feeds into portfolio decisions.

AI optimisation algorithms help construct optimal portfolios. These algorithms consider risk tolerance, return goals, and investment limitations. By using these tools, portfolio managers can create portfolios designed to maximise returns while minimising risk.

Risk Management

AI is changing how investment decisions are made. The AI algorithms can analyse massive amounts of historical market data and complex risk models.

The analysis provides a deeper understanding of individual asset risk and the overall portfolio’s exposure. With this knowledge, investment managers can proactively identify potential risks and develop strategies to lessen them.

AI offers real-time risk monitoring. An AI-powered system continuously tracks portfolio performance, alerting managers to any significant changes in risk. This allows for swift adjustments as market conditions evolve.

Automated Trading

Traditional automated trading tools execute trades based on pre-programmed instructions from human traders. These tools function within the parameters set by the user and can’t analyse markets on their own.

AI offers truly independent systems with tools that can analyse markets using technical and fundamental analysis with minimal human input.

AI uses sentiment analysis, ML, and complex algorithms to process vast amounts of information and identify trends. This data-driven approach removes the emotional bias that can affect human traders.

Case Studies

The asset management industry is seeing a rise in firms using AI to improve performance. A recent example is Deutsche Bank’s collaboration with NVIDIA. This multi-year project aims to integrate AI across their financial services. This includes virtual assistants for easier communication and AI-powered fraud detection. The bank expects faster risk assessments and improved portfolio optimisation.

Morgan Stanley is also making strides in AI adoption. Partnering with OpenAI, their financial advisors now have access to a massive research library at high speed. Advisors can explore client portfolio strategies and find relevant information in seconds, leading to better-informed advice.

Future Prospects

A PwC report predicts artificial intelligence will significantly boost global GDP, contributing up to $15.7 trillion in 2030. This advancement could reshape asset management in the coming years, leading to entirely new business models and investment strategies.

One future possibility involves fully automated investment platforms powered by AI. These platforms would manage investment portfolios with minimal human involvement and use real-time data analysis to create personalised investment plans.

Moreover, AI could pave the way for more dynamic investment strategies that respond to market changes. By constantly analysing market conditions, AI can automatically adjust investment portfolios to optimise returns and minimise risks. This could lead to more resilient and adaptable investment systems that are better equipped to navigate various market environments.

  • Artificial Intelligence in FinTech

Data analysis is critical for predicting risks and returns. The ever-growing size of data has overwhelmed human capacity. This is where artificial intelligence (AI) comes in.

AI is transforming the financial sector by automating routine tasks and efficiently analysing large and complex data sets. It can analyse vast amounts of data with unprecedented speed. The instant but comprehensive insights that this capability provides lead to significantly improved accuracy.

Introduction to AI in Financial Forecasting

Financial forecasting can be challenging for smaller businesses. They often rely on assumptions and human judgement. This can result in inaccuracy, especially when unexpected events occur.

AI can analyse massive amounts of data to find hidden patterns that drive revenue. It automates routine tasks and enables a more detailed analysis than humans can achieve on their own.

Predictive Analytics

By automating data processing and interpretation, AI empowers financial teams to make informed decisions based on a strong analytical foundation. It goes beyond basic analysis by employing advanced algorithms and machine learning (ML) to extract valuable insights from data.

This not only improves the accuracy of forecasts but also unlocks a deeper understanding of market complexities that were previously out of reach.

Risk Assessment

AI algorithms use advanced data processing to spot patterns, unusual activities, and connections that traditional methods might miss. 

By training ML models on past data, AI can learn to identify patterns associated with fraud. These models then analyse new transactions, compare features, and flag potential problems in real-time.

Real-time Data Analysis

Slow reporting and analysis have hindered companies’ ability to adapt. AI-powered systems overcome these issues by enabling real-time analysis and decision-making.

AI’s ability to process massive amounts of real-time market data helps financial experts identify opportunities and adapt to market shifts quickly. This translates to increased resilience and competitiveness for businesses.

Case Studies

Financial institutions are increasingly using AI to improve their forecasting and data analysis for managing operational risk. This trend is likely to continue as IndustryARC expects the AI market to reach US$400.9 billion by 2027, growing at a compound annual growth rate (CAGR) of 37.2% during the forecast period of 2022–2027.

Deutsche Bank‘s collaboration with NVIDIA on “Financial Transformers” shows the potential of AI for early risk detection. These models can identify warning signs in financial transactions and speed up data retrieval, helping banks address potential problems quickly and ensure data quality.

AI also plays a key role in anti-money laundering (AML) efforts. By analysing transaction patterns, customer behaviour, and risk indicators, AI can identify suspicious activities for investigation. This not only improves detection rates but also streamlines the process. Google Cloud’s AML AI is a prime example; it helped HSBC find many more real risks while significantly reducing false positives, saving them time and resources.

Future Prospects

AI in finance is expected to significantly reshape financial forecasting. Analysts and executives will see widespread AI adoption for tasks like data analysis, pattern recognition, and automation. This trend is driven by the projected growth of global AI in the finance market. A report by Research and Markets predicts it will reach $26.67 billion by 2026, growing at a rate of 23.1% each year. 

For investment firms, AI can make highly accurate forecasts and execute complex trading strategies, optimising investment decisions and returns. Banks will also benefit from AI’s capabilities. AI-powered data analysis can give banks a deeper understanding of their customers, enabling personalised financial services. Chatbots and robo-advisors used for customer service and financial planning will continue to evolve, becoming more advanced and even more human-like in their interactions.

  • Artificial Intelligence in FinTech

Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new…

Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new heights, including in the insurance industry.

Financial technology development has offered a better customer experience with enhanced accessibility and convenience. Mobile banks and digital wallets make it possible to contact the customer service team through online platforms. With the help of AI, FinTech companies escalate their services by offering more personalised, prompt, and efficient service.

AI Chatbots and Virtual Assistants

Conversational AI, which focuses on creating human-like interactions like chatbots and virtual assistants, improves customer service efficiency.

Chatbots are automated programmes that promptly address customer service queries. They can assist customers with inquiries and provide support for product information, account balances, or transaction details. AI-powered chatbots can give an immediate response and handle multiple customers at the same time.

Meanwhile, virtual assistants are voice-activated apps that can comprehend and carry out tasks based on users’ commands. These assistants offer personalised support by understanding the customers’ needs. For instance, they can deliver investment guidance tailored to customers’ risk tolerance and financial objectives.

These AI solutions can also assist human assistants by handling routine tasks, allowing them to focus on more complex work. Thus, the employment of AI assistants can reduce operational costs and effectively allocate resources to more important tasks.

Personalised interactions with AI

This approach can provide more personalised interactions by using algorithms and predictive tools to understand and respond to each customer’s preferences. AI algorithms can analyse large datasets of customers’ past interactions, browsing behaviour, and demographic information.

Meanwhile, predictive analytics tools can be used to anticipate customer needs and offer relevant financial products or services. These recommendations are constantly updated based on real-time client interactions and feedback.

24/7 Support

AI-powered customer service has the benefit of around-the-clock availability. It can operate continuously without being bound by office working hours like human-based customer service. Faster response times and enhanced availability help FinTech companies improve overall customer satisfaction.

Case Studies

Paypal, a digital wallet company, is one of the FinTech companies that has successfully used AI to improve its customer service. After implementing chatbots, PayPal experienced a 20 percent decrease in customer support costs and a 25 percent increase in user engagement. These chatbots can handle routine inquiries, resolve issues, and make personalised product recommendations.

Another example is Citi, a US retail bank that developed an AI-powered Customer Analytic Record (CAR). This programme can consolidate customer data, including financial records, used products, and interactions across online banking. The data is linked to automated decision-making AI software for analysis. The system can then recommend personalised offers to customers via text and mobile banking.

Future prospects

According to David Griffiths, Citigroup’s chief technology officer, AI has the potential to revolutionise the banking industry and improve profitability. With the continuous development of AI technology, the fintech industry can further improve its customer service.

Ronit Ghose, another executive at Citigroup, predicts that in the future, every client will have an AI-powered device in their pocket. This innovation will improve their financial lives with enhanced AI in customer service.

However, there are still concerns about AI’s scalability limitations in handling vast amounts of tasks. In addition, AI’s access to customers’ data makes security an important area to ensure its credibility. FinTech companies should ensure digital compliance to earn customers’ trust.

  • Artificial Intelligence in FinTech

The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer…

The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer experience to automating menial tasks. However, many are still cautious about using AI in certain areas, such as regulatory compliance management.

Given the continuously evolving legal requirements, good regulatory compliance management is crucial for banks. AI solutions can help effectively manage compliance by automating repetitive tasks, detecting suspicious activity, and providing real-time insights.

Automated compliance monitoring with AI

Artificial intelligence allows banks to perform continuous tasks around the clock with automated compliance monitoring. The previously labour-intensive work can be done more efficiently to ensure the bank follows all regulatory obligations.

The bank’s compliance teams usually handle monitoring processes, but AI automation can reduce costs. The compliance team can also focus on more important tasks rather than repetitive work.

The increased efficiency also means reduced compliance risk and non-compliance damage like fines.

Risk management

Financial institutions face regulatory compliance risks in various areas, which can lead to legal sanctions, financial loss, or a bad reputation. Advanced AI solutions can aid in risk management by identifying and mitigating risks more effectively.

AI-powered solutions can develop more accurate risk models and provide real-time responses. Many banks use this technology to help streamline compliance while improving the security of sensitive financial data. Furthermore, AI can detect compliance gaps and ensure adherence to laws and regulations.

Data analysis

AI can quickly analyse large volumes of data, a novel capability in the industry. A data analysis system can be designed to keep track of the latest regulatory changes and ensure the bank remains compliant.

Machine learning models can identify suspicious patterns and detect anomalies to report any breach of regulation. They can also analyse historical data and predict compliance risks. These allow banks to mitigate risks and address compliance issues before they escalate.

Case studies

Several banks have successfully used AI for regulatory compliance solutions. HSBC, for instance, uses AI-powered Know Your Customer (KYC) verification. This system can analyse customer data quickly, identify potential risks, and alert compliance officers for investigation. This bank also used Google Cloud’s Anti Money Laundering (AML) AI to combat and detect fraudulent activities in real-time. With these, HSBC has reduced the verification time by 80 percent and experienced a significant reduction in false positives.

Meanwhile, Danske Bank has also earned benefits from using fraud detection AI. The bank witnessed a 60 percent reduction in false positives and a notable decrease in fraudulent activities.

Future outlook for AI in regulatory in compliance

AI solutions are predicted to fundamentally change financial institution compliance management in the next five years, according to McKinsey. In the future, implementation for regulatory compliance in banks will be more widespread. Over 80 percent of C-level executives who participated in an Accenture survey planned to commit 10 percent of their AI budget by 2024 to address regulatory compliance.

AI offers many benefits, and as accessibility to this financial technology increases, more financial institutions will be inclined to adopt it, according to the Financial Stability Review.

Technology will evolve, giving better automation capabilities, more extensive data analysis, and enhanced interpretation. This could further reduce the manual effort required in the banking industry.

As adoption increases, ensuring the AI systems used are ethical and unbiased is necessary. Thus, banks need to provide transparency for AI in banking and adherence to guidelines.

  • Artificial Intelligence in FinTech

Financial service sectors are undergoing significant transformation driven by the adoption of AI.

From established institutions to innovative FinTech startups, financial organisations are embracing AI technology to improve their offerings and operations.

A report by Statista projects global investment in AI for financial services to reach a staggering $26.5 billion by 2025, highlighting the growing importance of AI in finance. Additionally, given the significant impact of AI technology, this article will explore the top 10 AI applications transforming the financial services sector.

Introduction to AI in Financial Services

The financial sector is grappling with a growing tide of data and intricate market dynamics. Furthermore, AI technology has emerged as a powerful tool to navigate this complexity. ML models, for instance, can analyse vast amounts of transaction data in real-time, identify unusual patterns, and flag potential fraudulent activities.

Moreover, AI’s impact extends to automating manual tasks that burden financial institutions. AI tools can efficiently process large datasets, generate reports, and handle administrative duties. Also, this shift towards automation allows financial institutions to focus their resources on higher-value and strategic endeavours.

1. Signifyd

Signifyd offers a comprehensive Commerce Protection Platform designed to empower businesses with a holistic approach to fraud and abuse prevention.

By using machine learning (ML) models, Signifyd’s Fraud Protection ensures exceptional accuracy in eliminating fraudulent transactions while automating order approvals. Additionally, this is further bolstered by Abuse Prevention, a feature that addresses customer abuse behaviours and simultaneously rewards legitimate customers.

2. KAI

Kasisto offers a conversational AI platform, KAI, designed to enhance customer experiences within the financial sector. KAI tackles two key challenges for banks: reducing call centre volume and empowering customers. Equally important, it achieves this by providing self-service options and solutions through AI-powered chatbots.

If a customer inquiry extends beyond the chatbot’s capabilities, KAI seamlessly transfers the conversation to a human customer service representative, ensuring a smooth handover and comprehensive resolution.

3. Entera

Entera, an AI application designed for residential real estate investors, streamlines the entire investment lifecycle. Combining SaaS tools and expert services, Entera empowers investors to buy, sell, and manage single-family homes. Furthermore, the platform grants access to a comprehensive database of on-market and off-market properties, simplifies transaction processes, and facilitates market trend discovery.

4. Range

Aimed at simplifying wealth management, Range offers a unique blend of AI technology and human expertise. This unique approach integrates investment management, tax planning, and estate planning services, all accessible through a user-friendly interface. Tailored to meet individual goals through a unified view of all financial activities, Range also offers clients the guidance of certified financial planners when needed.

5. Zest AI

Zest AI uses ML and artificial intelligence to address challenges in credit risk assessment for financial institutions. Their platform analyses vast datasets to identify patterns missed by traditional models, addressing longstanding challenges faced by financial institutions. Also, this AI technology aims to reduce lending bias, improve risk prediction, and expand access to credit for borrowers.

6. Upstart

Upstart is a fintech company using AI technology to improve credit accessibility. Their AI-powered lending platform assists financial institutions in making informed lending decisions by analysing a broader spectrum of data beyond traditional credit scores. This approach aims to expand credit inclusion, allowing borrowers with limited credit history to qualify for loans.

7. Proofpoint

Proofpoint offers a suite of cybersecurity solutions designed to shield organisations from sophisticated cyberattacks and compliance concerns. This AI application addresses people, data, and brand protection, encompassing areas like email security, data loss prevention, and threat intelligence. Recognizing people as the most susceptible targets, Proofpoint prioritises a human-centric approach to ensure the very foundation of an organisation’s security posture is fortified.

8. Brighterion

Brighterion tackles complex decision-making across industries like finance and healthcare with its unique model-based AI technology This model-based system utilises Smart Agents, enabling it to personalise, adapt, and continuously learn.

After analysing and observing data, the platform creates virtual profiles that update in real-time. This allows for a holistic one-on-one analysis, granting organisations a comprehensive 360-degree view of each entity’s behaviour.

9. Kavout

Kavout stands out in the industry by harnessing the power of ML and quantitative analysis. This approach allows them to process vast amounts of unstructured data and identify real-time patterns within the financial markets.

One of Kavout’s core solutions is the K Score, an AI-powered stock ranking system. Furthermore, by analysing this massive data pool, the K Score condenses the information into a single numerical ranking for each stock.

10. Trumid

In the fixed-income trading space, Trumid is a company using advanced analytics and AI to optimise the credit trading experience. Their suite of data-driven tools and proprietary Fair Value Model Price offers real-time pricing intelligence for over 20,000 USD-denominated corporate bonds. In addition, this engine analyses and adapts to market fluctuations, equipping traders with valuable insights to guide data-driven trading decisions.

  • Artificial Intelligence in FinTech

Traditional evaluation processes for credit scoring and analysis for risk management are being elevated with AI.

This innovation is driving financial inclusion for people around the globe who don’t have traditional access to financial institutions. Equipped with the correct algorithm and capability to assess big data sets accurately, AI is the ideal assistant.

Using a machine learning model, AI in credit scoring will continue to develop and upgrade itself the more we use it. New advanced algorithms can be expected. AI will be able to process bigger sets of data and produce more accurate results. This means a bigger scope of potential borrowers can be accessed, while making the lenders’ work lighter.

As has been seen, this function of AI is used in real-time by several US-based finance companies, such as Ocrolus that provides financial documents review services. They’re using AI to achieve 99% accuracy in their results.

The next step to further AI’s advances is by putting more effort in training it, making it a sharper tool.

How AI is becoming essential to credit scoring

Credit scoring is one of the main ways to assess potential borrowers and help decide whether they’re eligible for mortgages, business loans, or even credit cards. It also helps determine the terms they are offered, and the amount they can borrow.

AI is essential in this area because much of credit scoring is dependent on providing financial evidence as a guarantee, usually in the form of employment payslips or assets. New potential borrowers are less likely to have assets and are in an economy where self-employed, contract, and gig work is increasingly the norm.

Then there are those who are ‘unbanked’, who don’t have any savings – that includes 1.5 billion people.

New technology means data sourcing can become broader and more inclusive. This creates new borrower categories to consider, making it possible for financial institutions to reach more borrowers who previously could not be assessed.

AI Boosts Accuracy and Efficiency

Credit scoring must be done thoroughly, and that is a process that takes time and effort when done manually.

Once the process is established, it can follow protocol and move much faster. AI’s power makes it much easier to go from identifying a new model for credit scoring to being able to roll it out reliably at scale

Machine learning means all data AI analyses feeds into the processing system. AI is trained by analysing a bulk of data consisting of transaction history, debt history, and payment history. All of which are the main points of traditional data scoring.

But, instead of only training to do this repeatedly and accurately, AI will detect previously unseen patterns. This will help predict future behaviours of potential borrowers, such as their probability of repaying on time, from groups that do not have good access to credit. 

AI in risk management and assessment

When it comes to risk management, the more accurate the analysis, the better. With AI evaluating larger sets of data with more data sources, the results can be more personalised.

The model also helps the system to monitor the activities in real time using advanced and adjusted tools. Therefore, the outcome itself will always be the most up to date and precise. In a more advanced scenario, the tools can even predict based on previous patterns, giving them a function to prevent.

Real-life, real-time examples

Aside from risk assessment and data analysis, AI also contributes to many other factors. It can be used for fraud detection based on patterns that it recognises. It can also create personalised offers based on an individual’s data analysis.

The usage of this type of AI and the tools it creates is already being applied. Enova, a US-based financial technology company, uses AI to complete its credit assessment. With more advanced updates every year, we can expect even more companies in different industries utilising AI.

The biggest challenge moving forward is how much effort we want to put in to evolve the AI we have now, as the complexity grows and bigger effort is needed. Evidently, AI banking solutions help bring huge impacts, so attention is now shifted to updating them furtther.

The assistance AI brings to overall credit scoring and risk management in general will easily outweigh the complexity of its introduction. The more patterns and data AI consumes, the more accurate the results and powerful its feedback loop. Credit scoring is possibly the most impactful application of AI in financial services for the future of consumers.

  • Artificial Intelligence in FinTech

Artificial intelligence is fundamentally changing how businesses operate, and the banking and finance sector is no exception.

Furthermore, the integration of AI into banking apps and services has driven a shift towards a more customer-centric and technologically advanced industry.

AI-powered systems improve efficiency and decision-making within banks – but they also offer significant cost reductions. A 2023 McKinsey report on banking highlighted the potential for AI to increase productivity by 5% and generate global cost savings of up to $300 billion.

Introduction to AI in Banking

Automation in banking has evolved rapidly… Starting from basic work and Robotic Process Automation (RPA), to deploying AI in data analysis and eventually to sophisticated applications that impact core areas like risk management and fraud prevention.

AI’s deployment in advanced data analytics helps combat fraud and improve compliance. Meanwhile, AI models can streamline anti-money laundering measures, completing tasks in seconds that previously took hours or days.

AI’s data processing speed allows banks to uncover valuable insights that fuel AI development in chatbots, payment advisors, and fraud detection. This translates to a better customer experience for a wider audience, potentially boosting revenue, lowering costs, and improving bank profitability.

Understanding Customer Behaviour

Successful applications in functions that represent relatively “easy wins” have helped shift the focus to customers.

AI unlocked a new level of customer understanding. By analysing everything from spending habits to online behaviour, AI usesd machine learning to predict customer behaviour and tailor services accordingly.

This deep insight helps banks with AI strategies to be proactive. For instance, AI can identify patterns that indicate a customer may soon switch banks. Armed with this knowledge, banks retain customers by offering personalised incentives or targeted offers.

AI analysis of customer data to gain insights into spending habits, savings patterns, and investment preferences. Banks can use these insights to tailor marketing campaigns, enhance customer service interactions, and create new products and services that directly address the evolving needs of their customers.

A rising demand for more personalised customer experiences has dovetailed with the development of generative AI. The latter’s ability to learn, create, predict – and then communicate, promises a further revolution in banking technology and strategies. It also offers a method of automating delivery of better customer experiences at scale.

Personalised Product Recommendations

By implementing AI models, banks can now offer products and services that are tailored to each customer’s unique financial situation and future needs. This shift towards personalised product recommendations fosters deeper customer relationships and loyalty.

Personalised product recommendations ensure customers are only approached with offers that are likely to interest them, optimising the cross-selling and up-selling of financial products. This targeted approach not only increases the success rate of product offers but also reduces the inefficiency of blanket marketing campaigns.

Better Customer Service

AI-driven chatbots are revolutionising customer interactions in the banking sector. These virtual assistants provide personalised, round-the-clock experiences. Powered by natural language processing (NLP), chatbots understand and respond to customer queries in a manner akin to human communication. 

This AI strategy allows customers to receive immediate assistance with any banking matter, eliminating the need for long queues or frustrating phone calls. Customers can get instant assistance with various banking matters – from checking account balances and transferring funds to even applying for loans – all through a simple conversation.

Case Studies

Facial and voice recognition are becoming increasingly sophisticated thanks to AI’s ability to analyse vast amounts of data and refine authentication processes. These advancements not only enhance security but also contribute to personalised customer experiences.

A recent example is NatWest, the first major U.K. bank to leverage AI-powered biometrics for remote account opening. Developed with HooYu, the system uses real-time biometric matching to verify a customer’s selfie against official identification documents.

Another example comes from JPMorgan Chase, where researchers use AI and deep learning techniques to develop an early warning system for malware, trojans, and phishing campaigns. This system can identify threats before they occur, providing crucial time for the bank’s cybersecurity team to take preventative measures. These approaches show how AI strategies are shaping the future of banking tech.

Future Outlook

AI has the potential to revolutionise how financial institutions operate and interact with customers.

There is a major security challenge that comes with it. Banks have to prioritise cybersecurity measures to keep sensitive data protected from unauthorised access or accidental disclosures. There are also serious privacy concerns over the use of customer data.

Financial institutions have their own unique vocabulary and styles of communication. While this may seem a disadvantage, these emerged for ease of communication and specificity – and that means AI will be able to both learn and use the same methods finance workers are versed in. AI will likely become a companion tool for individuals within the industry, just as it will be for customers of it. Each will empower and improve the other.

  • Artificial Intelligence in FinTech

The financial services industry has always been racing to implement the newest technologies. Back in the 1960s, various financial institutions…

The financial services industry has always been racing to implement the newest technologies. Back in the 1960s, various financial institutions competed to introduce ATMs. In the 2020s, it’s AI’s turn to deliver the utmost value to fintech customers.

Modern finance infrastructure relies on AI-based fintech trends and solutions. Applications such as Venmo, Paypal, Wise, Apple Wallet, and other apps are the primary examples. With them, users can purchase insurance, apply for loans, or buy cryptocurrency without leaving their homes.

With the growing demand for fintech services, the rise of AI is rapidly reshaping the future of fintech itself. According to NVIDIA’s State of AI in Financial Services: 2024 Trends Survey Report, 43 per cent of global financial services professionals already use generative AI in their organization. Forty-six per cent of them are already using large language models (LLMs), too.

Catching up with AI trends is mandatory in maintaining a competitive edge. The NVIDIA report reveals that 97 percent of surveyed companies plan to quickly invest in more AI tools.  By next year, projections suggest that the global AI in finance technology market will rise to $26.6B.

Here are ten of the top AI trends expected to influence the fintech industry:

1. Customer Insights

Many AI tools enable analysts to crack customer behaviour and preferences. From the data, fintech companies can craft even more personalised experiences.

Customer insights can be inferred from various sources. For example, HSBC’s AI tool analyses a customer’s transaction history, coupled with their social media activity, to provide investment advice and product offerings. The approach has been said to improve customer satisfaction and retention rates. 

Another digital banking company, Revolut, uses machine learning algorithms to perform similar tasks. It provides AI-based budgeting and investment advice, as well as financial planning strategies.

2. Robo-advising

More financial institutions are exploring chatbots and virtual assistants with the ability to provide recommendations. According to research by Polaris, the Robo-advisor market is anticipated to grow from $7.39B in 2023 to $9.5B in 2024.

NVIDIA’s report also reveals that 34 per cent of financial services professionals sought AI’s help to enhance the experience of their customers. For instance, Bank of America’s virtual assistant, Erica, is equipped with AI insights to provide customers with real-time assistance.

3. Customer Onboarding with AI

It is commonly known that customer onboarding processes, especially in financial services, are often time-consuming. Many companies are looking to counter this by using AI tools that can automate compliance checks and document processing.

For example, the Oxford startup Onfido uses its proprietary AI, Atlas, to automate identity verification during customer onboarding. Atlas’s method include cross-referencing documents like passports and driver’s licenses with facial biometrics.

4. Robotic Process Automation (RPA)

Robotic Process Automation, as the name suggest, is a way to automate repetitive tasks. In various companies across the world, this technology has been transforming back-office operations.

By increasing effectivity, RPA allows companies to focus on value-added activities. JPMorgan Chase, for example, is able to cut the time to analyse legal documents through its COIN (Contract Intelligence) platform. The bank claims that COIN allows it to reallocate its resources to more strategic business endeavours.

5. Investment Management

More often than not, companies that use artificial intelligence systems to manage their investment benefit from better portfolio diversification. Independent investors who have converted to AI-driven services, too, seek ways to maximise the returns on their investment.

Wealthfront, a California-based investment firm, is a standout example of how a company can wield AI to improve its investment services.

Its platform formulates personalised investment plans based on risk tolerance and financial goals. With Wealthfront, investors also gain access to continuous portfolio optimization and tax-efficient investing.

6. Credit Scoring with AI

Traditionally, scoring models only process limited data. This can often lead to biases, especially for outdated models. In comparison, AI-based credit scoring that analyze broader data sources can assess creditworthiness in a more accurate manner.

This means improved access for underserved populations, on top of reducing default rates for lenders. California-based Zest AI, for instance, offers an AI-powered credit scoring platform that uses a tool called FairBoost to give a more holistic view of a borrower’s creditworthiness.

Ant Financial from Alibaba Group also utilises an AI tool called Zhixiaozhu 1.0 in credit scoring and risk management. Similarly, it uses machine learning algorithms to assess creditworthiness based on alternative data sources.

7. Regtech

Regulatory technology, which demand jumped last year, is a resource-intensive area for financial institutions. Therefore, AI automation has been a huge help in streamlining its processes.

In the field, artificial intelligence helps to guarantee financial institutions adhere to regulatory standards more efficiently and effectively. For example, De Nederlandsche Bank uses AI data analytics to detect networks of related entities. The process assesses the exposure of financial institutions to networks of suspicious transactions.

8. Payment Processing with AI

A lot of fintech companies are looking into AI to perfect their payment processes in terms of speed and security. The integration results in increased customer satisfaction, both for B2B and B2C companies.

The multinational finance company Stripe, Inc., for example, use AI tools to empower its digital payments processing. Now, customers can manage recurring billing effortlessly thanks to its advanced AI agents.

Stripe has also collaborated with Microsoft’s Azure OpenAI team to integrate GPT-3 for its support services.

AI improves the security and efficiency of blockchain and cryptocurrency transactions drastically. Some tools can perform difficult tasks such as predicting price movements, and optimise trading strategies.

A standout example is the American blockchain firm Chainalysis. For some time, the company has been helping prevent fraud and other illicit activities in the crypto space.

10. AML Compliance

Created to prevent financial crimes, Anti-Money Laundering (AML) regulations can benefit from the use of artificial intelligence. When integrated into the system, AI tools can efficiently detect malicious activities, which results in expedited AML processes. 

For example, the financial crime detection company AyasdiAI creates AI application Sensa to help institutions with anti-money laundering (AML) compliance. AyasdiAI’s platform identifies suspicious activity patterns that traditional methods might miss. Its method reduces false positives in AML compliance efforts and increases overall accuracy.

AI in Fintech’s future

The trends outlined in this article represent the future of the fintech industry.

AI’s role in fintech will only continue to grow with more companies investing in its development. Soon, artificial intelligence will take on more sophisticated tasks that add to the value of fintech products and services.

  • Artificial Intelligence in FinTech

Satya Mishra, Director, Product Management at Amazon Business, discusses how CPOs have become an important voice at the table to drive digital transformation and efficient collaboration.

Harnessing efficiency is at the heart of any digital transformation journey.

Digitalisation should revolve around driving efficiency and achieving cost savings. Otherwise, why do it?

Amazon is no stranger to simplifying shopping for its customers. It is why Amazon has become a global leader in e-commerce. But, business-to-business customers can have different needs than traditional consumers, which is what led to the birth of Amazon Business in 2015. Amazon Business simplifies procurement processes, and one of the key ways it does this is by integrating with third-party systems to drive efficiencies and quickly discover insights. 

Satya Mishra, Director, Product Management at Amazon Business, tells us all about how the organisation is helping procurement leaders to integrate their systems to lead to time and money savings.

Satya Mishra: “More than six million customers around the world tap Amazon Business to access business-only pricing and selection, purchasing system integrations, a curated site experience, Business Prime, single or multi-user business accounts, and dedicated customer support, among other benefits.

“I lead Amazon Business’ integrations tech team, which builds integrations with third-party e-procurement, expense management, e-sourcing and idP systems. We also build APIs for our customers that either they or the third-party system integrators can use to create solutions that meet customers’ procurement needs. Integrations can allow business buyers to create connected buying journeys, which we call smart business buying journeys. 

“If a customer does not have existing procurement systems they’d like to integrate, they can take advantage of other native tools, like a Business Analytics dashboard, in the Amazon Business store, so they can monitor their business spend. They can also discover and use some third-party integrated apps in the new Amazon Business App Center.”

Why would a customer choose to integrate their systems? Are CPOs leading the way?

Satya Mishra: “By integrating systems, customers can save time and money, drive compliance, spend visibility, and gain clearer insights. I talk to CPOs frequently to learn about their pain points. I often hear from these leaders that it can be tough for procurement teams to manage or create purchasing policies. This is especially if they have a high volume of purchases coming in from employees across their whole organisation, with a small group of employees, or even one employee, manually reviewing and reconciling. Integrations can automate these processes and help create a more intuitive buying experience across systems.

“Procurement is a strategic business function. It’s data-driven and measurable. CPOs manage the business buying, and the business buying can directly impact an organisation’s bottom line. If procurement tools don’t automatically connect to a source of supply, business buying decisions can become more complex. Properly integrated technology systems can help solve these issues for procurement leaders.”

Satya Mishra, Director, Product Management at Amazon Business

Beyond process complexity, what other challenges are procurement leaders facing?

Satya Mishra: “In the Amazon Business 2024 State of Procurement Report, other top challenges respondents reported were having access to a wide range of sellers and products that meet their needs, and ensuring compliance with spend policies. 

“The report also found that 52% of procurement decision-makers are responsible for making purchases for multiple locations. Of that group, 57% make purchases for multiple countries.

“During my conversations with CPOs, I hear them say that having access to millions of products across many categories through Amazon Business has allowed them to streamline their supplier quantity and reduced time spent going to physical stores or trying to find products they’re looking for from a range of online websites. They’ve also shared that the ability to ship purchases from Amazon Business to multiple addresses has been very helpful in reducing complexity for both spot-buy and planned or recurring purchases. Organisations may need to buy specific products, like copy paper or snacks, in a recurring way. They may need to buy something else, like desks, only once, and in bulk, at that. Amazon Business’ ordering capabilities are agile and can lessen the purchasing complexity.”

How should procurement leaders choose which integrations will help them the most? 

Satya Mishra: “At Amazon Business, we work backwards from customer problems to find solutions. I recommend CPOs think about what existing systems their employees may already use, the organisation’s buying needs, and their buyers’ typical purchasing behaviors. The buying experience should be intuitive and delightful. 

“Amazon Business integrates with more than 300 systems, like Coupa, SAP Ariba, Okta, Fairmarkit, and Intuit Quickbooks, to name just a handful. With e-procurement integrations like Punchout and Integrated Search, customers start their buying journey in their e-procurement system. With Punch-in, they start on the Amazon Business website, then punch into their e-procurement system. With SSO, customers can use their existing employee credentials. Our collection of APIs can help customers customise their procure-to-pay and source-to-settle operations. This includes automating receipts in expense management systems and track progress toward spending goals. 

“My team recently launched an App Center where customers can discover third-party apps spanning Accounting Management, Rewards & Recognition, Expense Management, Integrated Shopping and Inventory Management categories. We’ll continue to add more apps over time to help simplify the integrated app discovery process for customers.

“Some customers choose to stack their integrations, while others stick with one integration that serves their needs. There are many possibilities, and you don’t just have to choose one integration. You can start with Punchout and e-invoicing, for example, and then also integrate with Integrated Search, so your buyers can search the Amazon Business catalog within the e-procurement system your organisation uses.”

Are integrations tech projects?

Satya Mishra: “No, integrations should not be viewed as tech projects to be decided by only an IT team. Integrations open doors to greater data connectivity and business efficiencies across organisations. Instead of having disjointed data streams, you can connect those systems and centralise data, increasing spend visibility. You may be able to spot patterns and identify cost savings that may have gotten lost otherwise. 

“It’s not uncommon for me to hear that CPOs, CFOs and CIOs are collaborating on business decisions that will save them all time and meet shared goals, and integrations are in their mix of recommendations. 

“One of my team’s key goals has been to simplify integrations and bring in more self-service solutions. In terms of set-up, some integrations like SSO can be self-serviced by the customer. Amazon Business can help customers with the set-up process for integrations as well.”

How has procurement transformed in recent years?

Satya Mishra: “Procurement is no longer viewed as a back-office function. CPOs more commonly have a seat at the table for strategic cross-functional decisions with CFOs and CIOs.

“95% of Amazon Business 2024 State of Procurement Report respondents say the purchases they make mostly fall into managed spend. Managed spending is often planned for months or years ahead of time. This can create a great opportunity to recruit other stakeholders across departments versus outsourcing purchasing responsibilities. Equipping domain experts to support routine purchasing activities allows procurement to uplevel its focus and take on higher priorities across the organisation, while still maintaining oversight of overarching buying patterns. It’s also worth noting that by connecting to e-procurement and expense management systems, integrations provide easy and secure access to products on Amazon Business and help facilitate managed spend.”

What does the future of procurement look like?

Satya Mishra: “Bright! By embracing digital transformation and artificial intelligence to form more agile and strategic operations, CPOs can influence the ways their organisations innovate and adapt to change.”

Read the latest CPOstrategy here!

Edmund Zagorin, Founder of Arkestro, discusses his company’s rise as a predictive procurement orchestration platform.

“What if there was a better way to compare quotes from suppliers?”

This question led Edmund Zagorin down a road of discovery which culminated in turning an idea into a start-up.

While working as a procurement consultant, Zagorin observed how much time his sourcing teams spent building Excel pivot tables. The problem? Category experts needed to identify potential errors in supplier submissions at the item level before an award scenario could be properly evaluated. Together with childhood friend Ben Leiken, who had risen to become an engineering and product leader at SurveyMonkey, the idea was to find a way to automatically pre-populate text in a sourcing project with little to no manual data entry required from procurement users of suppliers. Leiken had seen firsthand the impact that so-called “smart defaults” could have on survey completion. And Zagorin knew that in procurement, more completions would mean more supplier offers, which could yield better commercial outcomes for the procurement team. Arkestro, then Bid Ops, was born.

Studies show that when procurement is able to predict a plausible range of commercial outcomes ahead of a supplier offer, there is enormous leverage created when the buying entity names the price. Summarising the past decade of research, Lewicki et al.’s 2007 “Essentials of Negotiation” states that “…whoever, the buyer or the seller, made the first offer… determined the final selling price, with higher final prices when a seller made the first offer than when a buyer made the first offer.”

For this reason, Arkestro customers began delivering material higher cost savings outcomes than traditional RFPs and RFQs, a fact that caught the attention of Ariba co-founder Rob DeSantis. Together, Zagorin and DeSantis brought together an experienced management team, led by IBM and Ariba alum Neil Lustig as CEO. Lustig’s experience as CEO of Vendavo, a predictive pricing company used by sell-side teams to achieve better negotiated outcomes, made him ideal to scale Arkestro into a global juggernaut.

Edmund Zagorin, Founder, Arkestro

Today, Arkestro is the leading predictive procurement orchestration platform that enhances the impact of procurement’s influence, especially for large manufacturing enterprises across any procurement activity and spend category that involves collecting a quote from a supplier. Arkestro turns the traditional procurement process on its head: instead of the supplier creating a quote or proposal and then a procurement analyst using competitive offers and benchmark data to decision the desirability of that offer or action an approval, Arkestro customers use a predictive model to benchmark a potential quote before contacting suppliers, putting procurement in a position of leverage to either ask for their desired outcome using an AI-generated Suggested Offer or generate an Instant Counter-Offer to any quote.

Arkestro then helps customers persistently monitor the changes in quoted price for this item across all procurement activities, tracking trends and changes and helping teams proactively uncover the optimal procurement configuration for each item and basket with respect to timing, geography, quantity, lead time and other attributes.

By embedding game theory, behavioural science and machine learning models directly into the procurement process, Arkestro enables customers to dramatically accelerate cost reduction projects, often with existing preferred suppliers and attain their best available cost outcome for every unique item more frequently and at greater scale across their spend. This predictive procurement approach is especially helpful for technical procurement categories such as highly engineered components, materials and capital equipment, as well as categories like metals, chemicals, food ingredients, MRO, packaging, logistics and even IT.

Enterprises who are on a journey to create sustainable and antifragile data quality for their procurement function are turning to Arkestro as the predictive approach eliminates the two manual steps that tend to introduce errors into item-level identifiers: the step where the supplier creates a quote, and the step where procurement analysts have to validate, correct, give feedback and approve it. By using a predictive model to generate and validate supplier offers, Arkestro offers a continuous improvement path for enterprises whose digital procurement journey includes cleansing item-level data to create a true item-based “data foundation.”         

Transformation journey

And since its founding in 2017, Arkestro has been on quite the transformation journey. The company has expanded rapidly and scaled its product – as well as for spend categories and industries served – globally. In a little over half a decade, Zagorin, Leiken and their team have created a true enterprise grade AI infrastructure platform that can be embedded into the likes of spend management giants SAP Ariba or Coupa or used as a standalone database and application.

Despite significant success in a relatively short space of time, Zagorin is keen to stress that his initial vision was to solve a problem that he was also experiencing in the market. “Our growth has corresponded to a great degree with a widening of the aperture of where we feel predictive technologies can make an impact for procurement teams,” he discusses. “I think one of the other things just from a paradigm standpoint is that procurement processes involve a lot of manually created data. There’s a lot of data entry on the supplier side, procurement side and on the stakeholder side throughout the process. Every keystroke in every process introduces the possibility of human error.”

Predictive procurement is a new approach that suggests the data before a human user enters it. What Arkestro has introduced is the idea of predictive and working with customers to apply that at different stages of the procurement process through AI. “One of the things that’s also been interesting, and you’ve seen this in other areas of AI, is that you can cross a threshold where at some point in the model it gets good enough that it really provides exponentially more value as it’s being used,” he says. “As opposed to software, which traditional software degrades over time, it gets stale and the interface feels clunky. As new interfaces come out, AI has almost the opposite dynamic where it actually gets better. It’s smarter by itself just by people using it. That’s also been pretty exciting to see.”

Procurement’s evolution 

Indeed, the procurement space is in a state of flux. Amid significant transformation driving the function forward, it has never been such an exciting time to be involved in the industry. The rise of AI and machine learning is having a seismic impact with there also being hopes that new technology could reduce the need to bridge talent gaps.

“If you asked five years ago what’s holding procurement back from digitally transforming the operation and living out your full potential, I think a lot of procurement professionals would’ve said how hard it was to hire,” Zagorin explains. “People were saying: ‘Oh we have data quality issues where it’s really hard to actually know what we’ve spent, what our spend per supplier looks like for our core geographies, let alone what we paid for each individual item. We went out and bought a bunch of digital platforms and we’re struggling to gain adoption which is related to the data quality issues.’ This is what I heard from executives when I was working in procurement. Because traditionally,  if you have a process and it’s not being consistently used, then it’s not going to accurately represent the most important attributes or business logic of the data that’s moving through it.”

Despite the positive introduction of tech innovation, procurement has also had its challenges. Supply disruption as a byproduct of COVID-19, wars in Ukraine and in Israel as well as inflation concerns, it is fair to say the function has never been more talked about in the C-suite.

“Boom, there’s the next wave of Covid, or suddenly there’s a war somewhere in the world,” he shares. “It has felt like there’s always something and it really creates context switching for procurement teams which is stressful, plus being bad for productivity. This is especially the case for digital transformation projects in procurement, and it’s also demotivating because it makes people feel like they’re not making progress. This then means that the length of the project elongates and you have this kind of stuck-in-the-mud feeling that it’s hard to get quick wins and generate momentum. That’s what customers are thinking about as they are looking in the market to find a true partner not just for their digital journey, but for their AI journey.” 

Given the speed of procurement’s evolution, there are voices that believe the function requires a rebrand. Gone are the days of procurement being regarded as a back-office function hidden away out of sight, today it stands as an exciting, dynamic force at the forefront of innovation. “I live in California where job titles are a little bit looser generally,” explains Zagorin.

“If we look at procurement needing a rebrand, the big challenge that I see with procurement is that the structure of a lot of these categories doesn’t necessarily correspond with either the activities associated with them or with the relationships with the suppliers within those categories. What we have in procurement with ‘category management’ is we’re frequently asking procurement professionals to be a jack of all trades and master of none within their categories. Perpetual ‘crisis-mode’ is not a recipe for letting up-and-coming procurement professionals develop the category knowledge and domain expertise that are traditionally necessary.”

Procurement’s bright future

Looking ahead, Zagorin believes there has never been a better time to be working in procurement. “The profession has a lot to offer, and it really is this huge engine of value creation at most big companies,” he explains. “Arkestro serves enterprise manufacturing companies typically with multiple plant locations which buy at both the corporate and the plant level creating a lot of item-level data quality issues. What we’re seeing is the ability for companies to get live on Arkestro in a matter of days and often deliver a payback period for their entire solution costs in a matter of weeks.

“If you look at deployments of enterprise technology five years ago, that’s a stark difference in terms of what procurement’s promising versus what it’s delivering and the time-to-value. We have a new generation of startups, from intake to tail spend to what Arkestro does, more on the strategic side and or on technical procurement categories and direct materials, often starting with a bill of materials and handling all the back-and-forth with the suppliers up to allocation, awarding and the purchase order. You have this cohort of startups that’s just getting bigger and more people are using us to run large physical manufacturing operations. There’s not a lot of direct competition in the space of these growth-stage startups. 

“I think what’s going to happen is more and more companies are going to say if it makes business sense and we think there’s tangible value in doing it, then let’s find a way to test and learn. Let’s find a way to try it out to implement it in one geography or for one business unit or category and just see how it works. Five years ago, it was always easy to say we’re too busy or we have other stuff going on. What’s changing today is if you’re not testing and learning constantly from new technology, you’re going to miss out because the stuff that’s happening right now is world-changing.

“Generative AI and novel technical approaches to on-demand superintelligence are going to be as impactful to many enterprises as the development of the internet, not to mention human society at large. The people who are playing around with it and staying curious and running experiments are going to create a lot more value. They’re going to have a lot more fun, and they’re going to build great teams and organisations that lay the groundwork for the next generation of procurement professionals.”

B2B procurement is headed for a new, more dynamic, digitalised era defined by a more strategic approach to traditional processes and new challenges.

The procurement industry isn’t a back-office function anymore. Much like the transition of IT departments from obscurity to the C-suite over the past 10-15 years, procurement is making its way into the limelight.

“We are entering a new era of smart business buying where senior leaders are understanding the impact procurement can have on efficiency and overall company success,” said Alexandre Gagnon, vice president of Amazon Business Worldwide, at a recent Amazon Business event attended by more than 1,000 procurement leaders across the public and private sectors.

“The procurement function is now cross-disciplinary, spanning both functional and strategic purviews as buyers are planning to invest more in technology and optimisation while future-proofing their companies and organisations,” added Gagnon.

Procurement’s transition

The 2024 State of Procurement Report released by Amazon Business in conjunction with the event points to an array of indicators that the nature of procurement is fundamentally changing. From the traditional procurement workloads concerned with day-to-day purchasing, to a more recently emerged responsibility of future-proofing the business against disruption (by another pandemic, for example), procurement’s goals are “ever-growing”.

In order to keep up, the discipline is “transforming at lightning speed,” claims Gagnon in the introduction to the report.

Data gathered from over 3,000 procurement professionals supports this inclusion. Key findings include the fact that 95% of decision-makers say their organisation currently has to outsource at least a portion of their procurement to third parties, the fact that 95% of decision-makers say their procurement function has “room for optimisation”, and 53% of respondents who say their procurement budgets will be higher in 2024 than they were this year.

Tech-driven procurement

Technology investment is expected to be high on the agenda, as procurement leaders attempt to bring increased visibility and resilience to their departments. A remarkable 98% of decision makers said they were planning to invest in analytics and insights tools, automation, and AI for their procurement operations, with the (anonymous) VP of purchasing at a major global bank in the US saying that “Making investments in the right tools and technology [is critical] because you rely on data as a procurement organisation. There is … spend data, contractual data, invoices, and more. Without the right tools in place, you can only do so much [with your data].”

Reflecting on the changing role of procurement in the modern enterprise, Gagnon added that “Ultimately, procurement not only keeps operations running, but plays an integral role in achieving key organisational goals, and with smart business buying, companies have procurement solutions to serve as a growth lever for organisations.”

By Harry Menear

AI and Machine Learning-powered analytics could help security teams flag and prevent fraud in their procurement functions.

Procurement fraud is costly and hard to prevent, but with the right tools, organisations could see red flags earlier and respond in time rather than too late.

According to the Association of Certified Fraud Examiners (CFE), organisations lose 5% of their annual revenue to fraud, with the median loss per case totalling $117,000, and the average being $1.7 million.

Supply chains and procurement functions are especially vulnerable to fraud—often comprising long and winding networks, intricate webs of relationships, vast inventory assets, and multiple transactions along the S2P journey. The procurement and supply chain functions of retailers and manufacturers are especially vulnerable.

Frequently, procurement fraud is the result of a malicious individual within the organisation, although vendors and partners can also be responsible. Bid rigging, intellectual property infringement, inventory theft, and product counterfeiting are all examples of occupational fraud within the procurement process.

To address these challenges, companies must implement proactive measures. The CFE report noted that nearly half of fraud cases occurred due to a lack of internal controls, or an overriding of insufficient existing controls. It also found that anti-fraud controls were effective, resulting in lower losses and quicker fraud detection.

Fraud is prone to thrive in the procurement process, and can have devastating consequences, but the fight against the threat isn’t hopeless, and new technologies are proving especially effective in stamping out the issue.

In addition to traditional anti-fraud measures like strengthening internal controls, performing due diligence, and conducting regular quality checks, organisations can fight fraud in their procurement and supply chain functions by harnessing the power of AI and Big Data.

Fighting fraud with Big Data

AI analytics of Big Data sets can do more than improve efficiencies and predict trends in the movements of goods; these types of analytics excel at pattern recognition and, once correctly trained, can identify subtle changes in activity within the procurement function and supply chain that could point to fraud.

According to Isabelle Adam, an analyst at the Government Transparency Institute in Budapest, and Mihály Fazekas, founder of the Institute and assistant professor in the School of Public Policy at Central European University, “With the increasing use of electronic and online administrative tools — such as e-procurement platforms — making administrative records readily and extensively available in structured databases, public procurement has become a data-rich area.”

This wealth of data, if improperly handled, can become a place for fraud to hide, but if big data analytics are applied, they argue, it “can serve as a tool for auditors to identify and prevent fraud and corruption.”

By Harry Menear

Next generation AI tools can offer unparalleled visibility into the sustainability of organisations’ supply chains.

There are increasing pressures on procurement departments to be a driving force in their organisations’ sustainable goals.

The process of buying, shipping, and generally moving physical products about is one of the larger sources of carbon emissions for the modern enterprise.

For consumer companies, supply chain operations typically account for more than 80% of greenhouse gas emissions, creating “far greater social and environmental costs than its own operations”, according to a study by McKinsey. The environmental impact of a company’s operations, and their extent into Tier 2 and Tier 3 emissions, is also becoming a more prominent part of the conversation, making the decision of who to partner with and for what more pertinent to an enterprise’s sustainability goals than ever before—especially as T2 and T3 emissions become the target of new ESG regulation.

The path to sustainable practice is increased visibility into procurement practices, supply chain impact, and the supply chains of ecosystem partners. Increasingly, procurement teams are artificial intelligence (AI) for these insights.

Responsibly sourced startups

The demand for AI-powered sustainability in the procurement sector is already driving investment in promising new tools. The Copenhagen-based startup Responsibly was founded in 2021, and in October 2023 managed to leverage its work on AI-driven sustainable procurement tools into a $2.4 million funding round, aiming to further develop its project of  “democratising access to sustainable procurement”.

The company combines an AI model with large data sets to allow users to analyse their suppliers and potentially take action to restructure their procurement practices. The data analysed relates to suppliers’ carbon emissions and links to deforestation, but also their gender pay gap, human rights records, and more. The company has already accumulated several high profile clients, including the CERN research facility.

Data-driven, sustainable decision making

The success (and sustainability) of a supply chain is, first and foremost, an issue of visibility. Decision-making to reduce carbon emissions, cut costs, and improve resilience is almost universally a matter of understanding the factors affecting what has traditionally been a very murky, complex, impenetrable system. Using AI to maintain visibility into upstream manufacturing, purchasing, and logistics channels is critical in a world where supply chains are more complex, and the critical eyes of regulators and other organisations within a company’s ecosystem are more prone to scrutiny, than ever before. 

For any organisation looking to operate more sustainably—especially in a climate of net zero commitments and increased regulatory scrutiny—the next generation of AI models, powered by advanced analytics, intelligent algorithms, natural language processing, and real-time processing of huge data sets, represents a way to understand the source to pay process on a more granular level than was previously possible, and a path to making the necessary decisions for a more sustainable supply chain.   

By Harry Menear

As AI continues to emerge in a big way, Vicky Kavan, Vin Kumar and Nicolas Walden explores what the AI opportunity is in procurement?

Procurement is a hard function to impress. Other parts of the business can afford to get carried away now and then, but not procurement. Everything in procurement comes down to finding value and then making sure you don’t overpay for it.

Artificial intelligence (AI) might seem like just the kind of emerging new technology that procurement would shy away from. But, as many procurement leaders already understand, this would be a big mistake. In our work with the world’s largest companies, we see two kinds of major emerging AI opportunities you won’t want to miss. The first group – how we execute our procurement using, for example, new autonomous sourcing systems – can save millions today. While the second – the advent of AI-driven automation and enhancements across almost every industry and areas of spend – will help save you even more tomorrow.

Savings today

In terms of the impact of AI, procurement executives predict that supply market intelligence (50% of respondents), contract management (43%) and bid optimization (37%) will be some of the greatest opportunity areas for AI technology.

Despite this, and even as most AI and generative AI systems remain pilot projects, autonomous sourcing systems are already transforming how procurement functions operate at large multinationals. Many procurement executives have told us that they find these systems, which can automate execution in either tactical or strategic areas and provide enhanced decision support, extremely valuable:

  • Clients tell us these systems are helping them reduce cycle times dramatically – from months to weeks or weeks to days – and cut costs by 10% or more. Supplier discovery?  Shorter. E-sourcing? Shorter. Contract development? Shorter. While it is in the early days, time savings of 30% or more can be possible.
  • When MTN Group, an African multinational telecommunications giant, installed its Procurement Cockpit platform, the system paid for itself in four weeks because the AI-enabled software quickly identified new opportunities, consolidated pricing insights from around the sprawling corporation and accelerated negotiation preparation.
  • These systems are now making themselves useful across a range of sectors. Procurement executives at a major U.S. retailer, major European telecom and major European energy company all told us that these systems have saved time and money. Use cases include replacing the need to write detailed requirements, sourcing questions and even contracts through the use of modified templates through to tactical price negotiations.

Strategic drive

From strategy to insights, sourcing and negotiating ­– to contract drafting and supply risk management – AI-enhanced systems will make procurement faster and simpler. Although feature sets and value propositions vary from vendor to vendor, promising  autonomous sourcing systems fundamentally change how technology engages with stakeholders using chatbot-style interfaces to summarise requirements as an output of discussions; search and identify providers of products based on a variety of market, process and business considerations; prepare request for proposals and contracts; and maintain a higher degree of compliance with regulations. Some of these systems can even execute simple one-round negotiations. At the moment, Globality, Fairmarkit and Pactum (for negotiations) are three of the biggest names in this space.

Savings tomorrow

Eventually, we expect that AI-enhanced functionality is likely to yield major cost savings in almost every spend area, business function and industry sector.

Contact centres or marketing services, for example, could already send out automated posts and even voice responses that mimic the voice of your choice. A travel agency might be able to supplement human customer service with a robot concierge, making it possible to achieve a much greater level of service than ever before. Such changes won’t happen immediately – implementing them is not a quick win – but AI enhancements will be a huge source of value and service improvements down the line.

Category managers, be advised: the general consensus among purchasing executives we polled recently is that fleet, digital tech, advertising and general equipment are the categories that will benefit most from AI-enabled technology.

Of course, as with most powerful tools, AI-powered services also create new sets of potentially considerable risks. For example, you will need to make sure that your contracts are clear about what your vendor can do with your data – can it be aggregated in a large language training model? If that model leads the company to develop a more advanced service, do you want to be compensated for your contribution? Are you covered for potential liabilities if you transfer customer data to your AI vendor and your customer’s information is somehow revealed? If you work with an AI vendor and create intellectual property on its platform, who owns that new product? There are many new angles and issues that you will need to consider.

Looking ahead

Over the next five to 10 years, AI is likely to transform many aspects of business, including procurement. Based on The Hackett Group’s analysis of 44 Level 2 processes across the source-to-pay, end-to-end process – for a company performing at the median of our database – there is a potential to reduce staff by up to 46% over the next five to seven years.

Clients have told us they see digital technology (including AI) as the most transformative trend facing procurement in the next few years (71%) – more important than data (51%) or environmental, social and governance, and sustainability (47%). For procurement professionals, how the work is done and where they will find value are both likely to change dramatically. Given the speed with which we expect these opportunities and their attendant risks to develop, now is a good time to start thinking about the opportunities AI can create for your team.

By Vicky Kavan, Vin Kumar and Nicolas Walden

At DPW Amsterdam 2023, Danny Thompson, Chief Product Officer at apexanalytix, tells us about the art of developing trust amid significant innovation in procurement.

Trust.

Apexanalytix needs to build quite a bit of it. As a company which protects $9 trillion in spend and prevents or recovers more than $9 billion in overpayments annually, its client portals actively support over eight and a half million suppliers.

Indeed, apex has revolutionised recovery audit with advanced analytics and the introduction of first strike overpayment and fraud prevention software. Today, apex is a leading global force in supplier management innovation with apexportal and smartvm, now the most widely used supplier onboarding, compliant master data management, and comprehensive third-party risk management solution in global procure to pay. With over 250 clients in the Fortune 1000 and Global 2000, apex is dedicated to providing companies and their suppliers with the ultimate supplier management experience. A big part of that experience is based on building trusted supplier-buyer relationships.

Danny Thompson is the Chief Product Officer at apexanalytix and has been with the organisation since July 2015. Now in his third role with the company in eight years, Thompson reflects on his journey with the organisation with positivity. “I came in as a product manager working on our portal product,” he tells us. “And after a short time, because I was a former customer, at Pfizer and International Paper Company, and was an internal voice of the customer, they ended up having me drive messaging with marketing. Recently, we hired a great new leader of marketing who has taken that over fully so I’m dedicated full time to product again. So it’s been a great experience for me.”

Gen AI surge

One of the hottest topics on the CPO agenda in recent months has been ChatGPT. Wherever you go within the industry, you’ll likely find a conversation being had about the technology’s possibilities, as well as perhaps its limitations or challenges – and Thompson is equally keen to explore.

Danny Thompson speaks with CPOstrategy at DPW Amsterdam 2023

“There is certainly a lot of attention being paid to gen AI in the industry, and within our company as well,” says Thompson. “I think it’s because of the shock value of ChatGPT hitting the world and people are really stunned by its ability to interpret natural language and come back with really good information in response to questions that are being lobbed at it. There’s a lot of excitement around what it could do as well as what other generative AI solutions can do to help solve procurement, supplier risk and supplier information problems. We are making progress, and have introduced some generative AI functions, but Generative AI presents some challenges right off the bat that we are working hard to solve as quickly as we can.”

One of these issues is the hallucination problem that is being questioned within the space. This is where AI tools like ChatGPT lack factual support for some of the information provided. “There’s a statement at the bottom of the page which states you can’t rely on results being factual,” Thompson affirms. “When it comes to supplier information and risk management, that’s a problem.”

Managing risk

And it is such an important sticking point that Thompson stresses when it comes to supplier risk information, it is about being careful that the usage of generative AI, in its current state, is used for guidance rather than fact-finding. “Another challenge is around leakage of sensitive information combined with contamination of sensitive or important information,” reveals Thompson. “We have a database of golden records for 90 million suppliers who are doing business with Fortune 1000 and Global 2000 companies. That is the best information we’ve been able to accumulate about suppliers and their relationships as a supplier to large companies. Some of that data is publicly available and some of it is more sensitive. We want to make sure we’re not loading that sensitive information into a generative AI function that might allow random people to access that information. We’ve got to be careful about that leakage of data.”

The opposite is true, as well.  Thompson reveals that his team asked the generative AI-tailored questions which they assumed would be pulled from their own database. The findings were less than ideal. “The responses had been contaminated with public information which was full of inaccurate data,” he tells us. “We’re figuring out how to draw those boundaries, as well—to protect sensitive data while also preventing contamination.”

Trust first

This showcases the importance of trust once again to an organisation like apex. The companies it serves are moving significant sums of money around and the potential risks are sizeable. For Thompson, there can be no greater responsibility when using AI tools. “The data must be either highly accurate or at least they understand the degree to which it’s not,” he says. “If you don’t understand that level of trust you can have in it, then you shouldn’t be using it yet.”

With an unprecedented amount of technological innovation at procurement’s fingertips, the industry is evolving at a rapid pace. It’s placed at a unique moment with digital transformation being swept up throughout the space. While this brings obvious advantages such as time and cost savings, it also means increased cybersecurity threats. “There are more threats coming in as a result of AI,” says Thompson.

“One of the biggest challenges our clients us our solutions to solve for is fraudsters trying to take over a supplier’s account and intercept their payments by submitting fraudulent account change requests. One of the typical ways companies catch these is very often the request is coming through very poorly formatted emails with bad grammar. But what we’re seeing is the bad guys have started using generative AI to create really convincing bank account change requests so there are increased threats to be aware of. But this increase in the availability of information is also make easier the whole process of knowing your supplier and knowing the risks associated with them. And Generative AI is going to allow you to quickly get help to understand how to mitigate a given risk much faster and easier than it’s ever been before.”

Shaz Khan, CEO of Vroozi, discusses why AI is the great equaliser for companies to optimise procurement.

In today’s ever-evolving business landscape, companies are facing a multitude of challenges when it comes to managing and controlling their spending. From global supply chain disruptions, outdated technology solutions, labor shortages and much more, these challenges have an immense impact on a company’s financial health and overall efficiency. Additionally, procurement teams are regularly tasked with new responsibilities beyond spend management and purchasing, such as managing supplier risk, building, and implementing CSG and ESG initiatives, studying economic trends to determine price elasticity, finding new sources of supply, and cleaning up disparate and dirty data. Yet most companies simply do not have the human capital or bandwidth to execute these areas with quality and control.

When it comes to bridging the gap between the obligations that procurement teams are tasked with and efficiently executing on these tasks, AI may be the great equaliser to help solve these problems. While AI has turned into somewhat of a buzzword in today’s market, there’s no doubt that the technology has powerful capabilities to truly transform procurement in the foreseeable future. For those changes to take place, it is important for procurement professionals to continue to articulate the problems they are facing on a daily basis, as this will force the industry to evolve and adopt the proper solutions for better business outcomes.

Shaz Khan, CEO and co-founder, Vroozi

The problems: Unchecked spending, outdated tech, and lack of governance

Irresponsible spending can wreak havoc on a company’s financial well-being. With non-managed indirect and direct spend categories, companies experience up to a 40% increase in costs, consequently eroding their gross margins and increasing operating expenses. This usually stems from lack of visibility into non-payroll spend categories, combined with old and antiquated technology solutions within enterprise infrastructure that makes it difficult to extract data, analyse spending patterns, and generate meaningful reports on total addressable spend (sound familiar?). Poor data quality and the need for data cleansing can impede effective spending management, leading to faulty decision-making that hinders procurement efforts.

Unchecked spending can also foster a culture of mistrust and overall decreased morale among employees. When employees perceive that their hard work and dedication are being undermined by wasteful spending practices, workers begin to feel disengaged — which leads to reduced productivity. When spending is not carefully managed, there is a risk that critical projects or departments may not receive the resources they need to thrive. This not only causes anxiety about the organisation’s financial health, but it also can lead to concerns about resource allocation and fairness. Therefore, it creates broader mistrust in organisational leadership.

One of the biggest culprits in inefficient spending management comes from a lack of visibility into supplier contracts, which stifles a company’s ability to identify cost-saving opportunities. Hidden fees, price escalations, and unexpected cost structures can be buried in supplier contracts. A lack of visibility can result in unexpected cost overruns, impacting the organisation’s budget and profitability. Departments may also struggle to fully understand the terms and conditions within these contracts, including performance expectations, delivery schedules, and penalty clauses. This lack of clarity can increase the risk of contract breaches, quality issues, or delivery delays.

The long-term benefits of incorporating AI into procurement

With more at stake within procurement departments than ever before, AI serves as a turbocharged catalyst for procurement teams to optimise their processes. Procurement leaders are increasingly delegated additional responsibilities and AI offers an invaluable assistant that can process, predict, and deliver information and outcomes without exhausting human resources. For example, predictive and smart reordering can keep items that require ongoing restocking on a regular purchasing cycle. AI can also help identify alternative sources or suppliers for this item that may offer additional cost-savings and attractive incentives. As this technology becomes increasingly more capable, it’ll save procurement departments hours of time — freeing up employee bandwidth to then focus on optimising supplier relationships and other strategic tasks.

Earlier, we discussed how unchecked spending leads to mistrust and disengagement within an organisation. AI can help re-establish morale and an engaged staff by gamifying the procurement process. For example, a company can create a scenario where employees and teams are rewarded with soft benefits for complying to procurement policies, reducing maverick spend, improving supplier relationships, or negotiating a new deal with a strategic supplier. These soft benefit rewards can be programmed into the system to track and signal when teams are hitting these goals. Gamification, particularly when entire teams are rewarded together, can foster camaraderie and a dynamic culture built around the thrill of victory, aligning employees with the company’s procurement strategies.

Ensuring a smooth transition to AI-driven procurement processes

When beginning the transition towards an AI-infused process, it requires an honest assessment of existing processes, data quality, and technology infrastructure to identify pain points and areas where AI can provide the most value. Integration will require some level of customization to meet the specific needs of your business, such as custom algorithms, workflows, or user interfaces. This is an ongoing process. Optimisation requires the continuous gathering of feedback from users and stakeholders to identify which areas are working well and which features need improving. Be prepared to adapt as you go along. AI is a rapidly evolving field, and we are in the very early stages of realising the true potential of this technology.

As the AI revolution takes place in procurement, employees need to be introduced to new technologies to understand the strengths and more importantly the limitations. However, when thinking of the big picture, Procurement teams must be prepared to upskill their talent pool and recruit new talent to maximise AI’s potential including investing in certifications in data science, cloud platforms, supply chain management, and data analytics. To reap the benefits of automation, data-driven insights, and enhanced decision-making, leadership requires teams that have skills to use and interpret AI tools effectively — particularly when it comes to data management. AI solutions rely heavily on data and procurement teams must know how to effectively manage this data, including data cleansing, integration, and analysis to ensure that the algorithms receive high-quality input data and large language models for accurate results and the promise of real predictive analytics.

The promise of a brighter future

This is also why collaboration between departments is essential. For AI technology to be implemented effectively, it requires synchronisation and cross-functional collaboration between IT, data science, corporate procurement, finance, and other departments. Companies that cultivate these collaborative ecosystems within their departments gain a strategic edge in terms of stability and future growth.

It’s important to note that while AI is a productivity and enablement tool, it is not a replacement for human intellect, willpower, and execution. Therefore, it’s essential to seek knowledge and expertise from insights from companies, networking groups, and individuals with practical experience in AI and GenAI capabilities. Remember, it’s important that you do not let AI drive your business, but rather let your business needs drive AI adoption. Define the specific problem that you aim to solve and determine if AI is the right tool to boost these areas.

Ultimately, the incorporation of AI into procurement processes holds the promise of a brighter, more efficient future for businesses. Procurement departments face many challenges but if they address these pain points with a strategic approach that involves the adoption of modern technology solutions while upskilling their workforce, businesses can expect to soon see enhanced visibility into their spending and gain a strategic edge in a competitive market.  One thing is certain, AI will transform the procurement professional and function into a data analytics and supplier relationship mastermind.

By Shaz Khan

CPOstrategy examines 10 of the best ways to use artificial intelligence (AI) in procurement

Artificial intelligence (AI) is one of the biggest buzzwords in procurement. Everyone wants to get their hands on it and introduce it into their strategies.

Particularly in procurement, AI is often talked about being the answer to all challenges. It can be used to overcome complex problems and deliver efficiency while also being introduced within software applications such as spend analysis, contract management and strategic sourcing.

In this article, we will list 10 of the best ways to use AI in procurement.

1. Machine learning spend classification

AI algorithms can help categorise, clean and classify data automatically. Machine learning spend classification helps detect patterns and uses them for prediction while allowing for better decision-making. Examples of spend classification techniques include supervised learning, unsupervised learning in vendor management and classification reinforcement learning. 

2. Natural Language Processing (NLP)

National Language Processing (NLP) is the branch of artificial intelligence focused on understanding, interpreting and manipulating human language. It can be used to gain valuable data and information to streamline time-consuming processes. Information contained in legal documents can be interpreted through AI for the procurement of relevant data. It allows procurement professionals to get ahead and use an AI assist engine to receive alerts to proactively monitor progress. It also allows for compliance over the life of multiple agreements with the same or several vendors.

3. Robotic Process Automation (RPA)

Robotic Process Automation (RPA) mimics human actions to eradicate repetitive tasks. While not strictly AI in the traditional sense, RPA does provide procurement with opportunities to improve process efficiency and is part of the wider family of AI. It can assist with the likes of contract management, input identification as well as purchase request and order submission, among more benefits.

4. Anomaly detection

With AI being able to process vast amounts of data quickly, it is able to stay up to date on the latest developments and changes in the procurement space at speed. Automated notifications on things such as anomalies, new opportunities and recommended activities allows for immediate action to be taken and provide suggestions on what should be done instantly. Rapid detection will ensure risks are mitigated and resolved before they become problems.

5. Purchasing

AI can be utilised to automatically review and approve purchase orders. Chatbots can be used to check the status of acquisitions or automatically approve virtual card payments. AI can analyse data and assess the reliability and quality of suppliers based on predefined criteria. This helps the purchasing team select the best suppliers quickly and accurately.

6. Contract management

Contract management can benefit through using AI to create, store, review, index, retrieve, analyse, negotiate and approve agreements. A big benefit delivered by contract management solutions that use AI is standardised metadata reporting which eliminates the need for category managers and legal counsels to manually read contracts to gain insights into the commercial part of their supplier relationships.

7. Supplier risk management

Supplier risk management is an important part of the procurement process and is around understanding what happens if a supplier fails to meet its obligations. To combat this, AI can be used to monitor and work out potential risk position through Big Data. Millions of different data sources are screened in order to provide alerts on potential risks within the supply chain.

8. Accounts payable automation

AI can automate most manual tasks in accounting such as data entry and invoice routing. Using AI for this substantially reduces procure-to-pay cycles, minimises the need for humans to get involved and integrates multiple workflows into a seamless process.

9. Strategic sourcing

Using AI in strategic sourcing is a key tool in a procurement practitioner’s arsenal. AI can be used to manage and automate sourcing events while also leveraging machine learning for the recognition of bid sheets, as well as specialised category-specific e-sourcing bots such as raw materials and maintenance.

10. Automated compliance

AI can also be used as a valuable tool for compliance officers to help work out potential risks, monitor employee behaviour, generate reports, provide recommendations as well as educating employees about the importance of compliance. For organisations without a source-to-pay system, compliance is a useful alternative and allows procurement teams to seamlessly compare payment terms, identify duplications as well as determine non-compliance.

Nigel Greatorex, Global Industry Manager at ABB, on how digital technologies can support decarbonisation and net zero goals

Nigel Greatorex is the Global Industry Manager for Carbon Capture and Storage (CCS) at ABB Energy Industries. He explains how digital technologies can play a critical role in the transition to a low carbon world by enabling global emissions reductions. Furthermore, he highlights the role of CCS and how challenges can be overcome through digitalisation.

Meeting our global decarbonisation goals is arguably the most pressing challenge facing humanity. Moreover, solving this requires concerted global action. However, there is no silver bullet to the global warming crisis. The solution requires a mix of investment, legislation and, importantly, innovative digital technologies.

Decarbonisation digital technologies

It’s widely recognised decarbonisation is essential to achieving net zero emissions by 2050. Decarbonisation technology is becoming an increasingly important, rapidly growing market. It is especially relevant for heavy industries – such as chemicals, cement and steel. These account for 70 percent of industrial CO2 emissions; equal to approximately six billion tons annually.

CCS digital technologies are increasingly seen as key to helping industries decarbonise their operations. Reaching our net zero targets requires industry uptake of CCS to grow 120-fold by 2050, according to analysis from McKinsey & Company. Indeed, if successful, it could be responsible for reducing CO2 emissions from the industrial sector by 45 percent.

A Digital Twin solution

ABB and Pace CCS joined forces to deliver a digital twin solution. It reduces the cost of integrating CCS into new and existing industrial operations. Simulating the design stage and test scenarios to deliver proof of concept gives customers peace of mind. Indeed, system designs need to be fit for purpose. Also, it demonstrates the smooth transition into CCS operations. Additionally, the digital twin models the full value chain of a CCS system.

Read the full story here

In early 2019, the Voluntary Health Insurance Scheme (VHIS) was introduced in Hong Kong by the Food and Health Bureau…

In early 2019, the Voluntary Health Insurance Scheme (VHIS) was introduced in Hong Kong by the Food and Health Bureau to regulate indemnity hospital insurance plans offered to individuals, with voluntary participation by insurance companies and consumers. The VHIS was designed as a means of encouraging and supporting customers to purchase private healthcare services and for Koh Yi Mien, Managing Director Health and Employee Benefits at AXA Hong Kong, this scheme represents a broader transformation of healthcare and insurance services. “Currently, the demand on healthcare in Hong Kong in the public sector is incredibly high with very long waiting times and waiting lists,” she explains. “As a result, people just aren’t getting timely access to treatment. The private sector in Hong Kong, which is world-class, has capacity. So, if we can rebalance and shift some of the elective work from public to private, it will free up more people to use the public service in a timely fashion.”

Yi Mien also points to a global drive for greater transparency, accountability, use of data and technology as well as promoting customer choice as key drivers of change in the insurance space. “It’s no longer a case of simply providing reimbursement to people when they need treatment,” she says. “It’s about being the patient’s partner throughout their whole life so that when they need healthcare, whenever and wherever they are, we are there to help and support them in their times of need.” 

The modern-day insurance customer is very different from the customer of the past. We live in times of greater access to information through the advent of social media and the increasing influence of the Internet and this has resulted in insurance customers being more knowledgeable about their conditions and asking more questions of their doctors than ever before. As a result, the balance between the customer and the healthcare provider is becoming more equitable. “Customers and patients, as a result, are becoming more demanding,” says Yi Mien. “Gone are the traditional ideas that doctor knows best. It’s not uncommon for patients to see their doctor with a list of demands, while expecting to be serviced.”

Running parallel to becoming more knowledgeable and demanding is the use of smartphones and how it has created a culture of service in an instant. When customers purchase etiquettes or use banking services, they expect the ability to be able to access and complete these transactions and services via their smartphone devices. Fewer and fewer people are accessing physical bank branches and the healthcare insurance sector, despite being still very traditional, is feeling the effects of this instant demand. “Healthcare is a very traditional sector sure, but asking patients or customers to book weeks in advance and telling them they don’t really have any choice is becoming increasingly unacceptable and so healthcare becomes a commodity,” says Mie Koh. “They, like any other customer, vote with their feet and want 24/7 access to quality healthcare without waiting directly from us as the insurer.”

The informed customer and patient have also transformed the relationship between customer and doctor. It is no longer a bilateral relationship and the entire healthcare ecosystem works to provide services from prevention right through to treatment. The result? Insurers like AXA work with customers before they are sick and encourage them to maintain their health, but they also work with clients during their illness and even afterwards AXA will continue to treat them in their rehabilitation. “During their healthcare journey, customers want some handholding in order to navigate the very complex healthcare system, to make sure they get the right healthcare provider, doctor and hospitals that are best for them in their time of need,” says Yi Mien. “This can only happen if we are using digital so that it becomes more real time.”

AXA has been embracing technology for a number of years to be able to serve and effectively work with its customers. It achieves this by starting with the definition of a product, because the product sets the rules. Yi Mien highlights that the rules would often be how AXA would spell out the terms and conditions, the provisions, but these rules also set the customer expectations. Throughout late 2018 and 2019, AXA has invested in digital to enable its customers to buy online, service online, claim online and check-up online. The company also launched a servicing app called Emma, a ‘digital companion’ that enables even faster service. Yi Mien describes this app as a true “health companion”. She is also keen to highlight that the technology is only part of the story. AXA has built a vast medical network with some of the leading hospitals and doctors and customers simply having to log into their companion app to be able to access this network at the touch of a button. “All they need to show is their digital card, their e-card, and with the QR code, the provider just scans it. All of the data is downloaded and all they need to do is sign, get their treatment, and then when they discharge, just sign that they have received the treatment and off they go,” she says. “The hospital will bill AXA directly so there’s no out of pocket. The data is also transmitted to AXA which means that we have more comprehensive and more reliable data.”

Comprehensive and reliable data is crucial to the technology journey of AXA, but it is also integral to the customer journey. With a customer’s entire electronic medical records stored effectively and securely, as Yi Mien notes, why would they go anywhere else? The data that an insurer handles is often complex in nature, but this data is processed through artificial intelligence, with AI being used to process claims more effectively and interpret the information to allow AXA to create rules and algorithms to better serve its customers. AXA also utilises AI through its companion app Emma. “Emma is our chatbot,” explains Yi Mien. “Emma has been built up based on a multitude of Q&As that our customer services team have recorded and collected over many months and years. As we continue to build, and more people use Emma, then the quality of the responses she has in her arsenal will improve.” In the first two months of operations, Emma recorded an accuracy level of 50%. Yi Mien firmly believes that as more people engage with Emma and as a result, the chatbot will evolve and become more of a real-time navigator that can direct customers across the whole ecosystem.

In the global discussion around AI, the topic of transparency is often a key point of debate. With governments around the world shining a spotlight on exactly what data is collected and how it is used, AXA ensures that it maintains an open and transparent dialogue with its customers. As customers engage with Emma and the companion app, they can at any time request their transcripts. Should they choose to speak with a human adviser, all calls are recorded and again they can access those recordings should they wish. Not only is this an example of AXA complying with global governing laws, it also highlights that the customer is at the very heart of every decision it makes and it maintains this as it continues to implement new technologies. “If you look at banking as an example, we all are so used to accessing our bank accounts at any time, be it through our phones or online,” says Yi Mien. “If we want to speak to someone, we can. If we want to go into a branch, we can. I believe this is the way to go with insurance as well. We make it easy for our customers to contact us. We are doing everything we can to allow that.”

“Healthcare is quite personal, so we are doing what we can to allow customers to speak to people, should they not wish to use our chatbot. These are very personal journeys and digital is still in its early days, so we really have to provide different avenues and channels for our customers to contact us.”

As Yi Mien notes, AXA designs its customer journey by starting at the product and going through all the way to treatment. The company makes every decision with the customer’s perspective in mind. As a doctor by trade, Yi Mien sees that all new products are designed by doctors because they understand how the patients move throughout the whole healthcare ecosystem. When AXA designs new products, it does not operate within a vacuum. It has a customer insight group, where around 1,000 customers operate as a real-time focus group in which AXA can test its products with. “When I think about future products, we will test with this group of people and get feedback to see whether we are aligned with the current customer need. So, it’s not just technology per se, but actually meets a customer’s needs,” she says. “One other area to make sure that we are doing the right thing, because technology also costs money, is to make sure that we are very robust in what we do. AXA is unique in that we sell life insurance, health insurance, employee benefits, and we also have P&C. So, being a multi-line insurer, we have the opportunity of having one approach and cross-selling across the business lines, which is a fantastic opportunity. We can only do that through technology.”

Over the course of her career, Yi Mien has been a champion of the transformative effect of technology in becoming a greater enabler for healthcare and healthcare insurance providers around the world. One area in particular that is close to her heart is the mental health space. In Hong Kong, the waiting time to see a psychologist is close to two years and if patients were to seek private care, it is an expensive solution. “Look at a country like Hong Kong, or Australia, they are so vast that there just aren’t enough practitioners to cover the breadth of the geography. Digital is the solution,” she says. “Digital enables people to seek, support and care at the time that is most convenient for them.”

“In the past two to three years, there has been a proliferation of digital tools. Recent studies have shown that digital tools are as good as, if not better, than in-person therapy because customers prefer to talk to a robot rather than face-to-face because they feel that the robot is not judging them.”

Another example that Yi Mien highlights is in the UK, where a VR program has been developed by programmers that is therapy through gameification. The treatment is consistent every time and because of its mobile platform, it is accessible. “We can provide it where you work,” she says. “That’s just one example as to how we can destigmatise mental health through technology.”

AXA operates within a broad healthcare ecosystem, an ecosystem made up of partners, providers and doctors and Yi Mien stresses that in the future of insurance, it will be impossible for insurers to control the ecosystem. “I don’t foresee a future where that happens,” she says. “Partnerships are incredibly important. Things are moving so fast there’s no way we can catch up alone. We need to have partners, collaborators, who are working together to ensure we are at the top of our game and at the forefront of innovation.”

“Over the course of our lives, so many different things can happen and so people will need better care and support. By having a collection of data that represents our customer’s needs we are able to push or suggest services that better meet those needs. In order for us to do that, we need to have players collaborate in the ecosystem. It’s imperative.”

As AXA continues this digital growth journey, the next few years will be defined by improving the agility of the digital companion in order to improve the interaction with customers. AXA will also be looking at developing a digital marketplace in which customers can go shopping within an AXA owned digital platform. For Yi Mien, though, the future is clear for AXA and in order to be successful, she feels it’s down to one thing. “AXA has a clear digital strategy for sure, where it will transform its digital system and build new IT infrastructure to transform the customer experience,” she says. “But the technology is only one part of the story.”

“Unless we can transform the customer experience to deliver a service they truly value, then technology doesn’t do anything. It’s important to recognise that technology is enabling us to transform healthcare, to make it easier, faster, and cheaper for people to receive care. That means in the long-term, sustainable healthcare and health services, which fits into sustainable insurance.”