Author

Arsalan Minhas

Published

3 April 2025

Estimated Read time

5Mins

Arsalan Minhas, AVP Sales Engineering, EMEA & APAC, at Hyland, on how AI revolutionising financial services

Artificial intelligence (AI) is revolutionising financial services, reshaping how institutions detect fraud, personalise customer experiences, and optimise investment strategies. From AI-powered chatbots assisting customers to machine learning models predicting market trends, the technology is driving unprecedented efficiency and insight.

Yet, alongside these advancements come new challenges. AI-driven scams are evolving in sophistication, algorithmic biases raise ethical concerns, and regulatory scrutiny is increasing. As financial institutions accelerate AI adoption, they’re walking the fine line between harnessing its benefits and mitigating its risks. 

AI in fraud detection and prevention – strengthening security measures

One of the most critical areas where AI has transformed financial services is fraud detection and prevention.

Traditional fraud prevention methods relied on static rule-based systems, which were often ineffective at identifying evolving threats. Such systems aren’t necessarily equipped to keep up with the sheer pace of financial service operations today, which has led to a surge of interest in automated alternatives.

AI, particularly machine learning algorithms, offers a dynamic solution by analysing vast datasets in real time to identify anomalies and potential fraud. AI also enhances biometric authentication methods, such as voice and facial recognition. This can ensure secure access to accounts, reducing the reliance on passwords, which are vulnerable to breaches.

According to a recent McKinsey report, AI-driven fraud detection systems can reduce financial fraud losses by up to 50%. Making them a crucial asset for financial institutions. These unprecedented levels of speed and versatility has made AI a priority for even the biggest players.

Of course, fraud detection is not without its challenges. Criminals are also leveraging AI to create sophisticated scams, such as deepfake-based identity fraud. And the introduction of new technologies can challenge cybersecurity initiatives.

With that in mind, financial institutions must constantly update their AI models to stay ahead of emerging threats. Regulatory compliance adds another layer of complexity, as AI’s decision-making much align with consumer protection laws and data privacy regulations like GDPR and CCPA.

The future of Customer Experience

On the customer-facing side of things, Artificial Intelligence is transforming the customer experience through hyper-personalised financial services. Gone are the days of generic banking interactions. AI now enables financial institutions to tailor services based on individual customer behaviours, preferences and financial goals.

Leading UK banks like NatWest and Lloyds Bank have invested heavily in AI-powered virtual assistants. NatWest’s digital assistant, Cora, has handled millions of customer interactions, providing real-time financial insights, bill reminders, and even fraud detection alerts. Similarly, HSBC uses AI-driven tools to analyse spending patterns and offer personalised financial advice. The ability to assess transaction data allows banks to recommend budgeting strategies, suggest tailored loan offers, and predict future financial needs, making banking more intuitive and customer centric.

AI-driven robo-advisors, such as those offered by Nutmeg and Moneyfarm, have revolutionised investment management by providing algorithm-based financial planning. These platforms leverage AI to assess risk tolerance, market trends, and historical data to offer personalised investment strategies with lower fees than traditional financial advisors. 

While such tools can be incredibly effective, they do raise concerns about data privacy and algorithmic bias. The more AI knows about an individual’s financial habits, the greater the risk of data misuse or bias in lending and investment recommendations.

Financial institutions must therefore ensure transparency and fairness in AI decision-making to build customer trust and meet regulatory regulations. The basis upon which customers share their personal data, and the protections that it is afforded, are a non-negotiable for any serious financial organisation.

Redefining market strategies in trading and investment

According to Deloitte, Artificial Intelligence is poised to be one of the most disruptive forces in investment management. High-frequency trading (HFT) firms now rely on AI algorithms to process vast amounts of market data within milliseconds. It also enables hedge funds and investment firms to predict market movements by analysing patterns from historical data, social media sentiment, and global economic indicators.

Leading firms like Man Group and XTX Markets have harnessed AI to enhance their trading strategies and portfolio management. Man Group, managing $175 billion in assets, utilises machine learning tools to develop its platform, ManGPT, to analyse trades and optimise investment decisions.

Similarly, XTX Markets, a London-based trading firm, employs advanced AI models to execute millions of trades daily, emphasising AI-driven strategies over sheer speed. Predictive analytics have become an indispensable tool in portfolio management, helping firms adjust their strategies based on real-time market fluctuations.

Naturally, these automated tools require to-the-second oversight from the business itself. The 2010 Flash Crash, in which the stock market plunged nearly 1,000 points within minutes, was exacerbated by algorithmic trading. AI-driven trading models can react unpredictably in volatile markets, amplifying risks if not properly regulated. Humanised AI – the combination of human and AI working in concert, rather than automated systems working in isolation – is crucial.

The future of AI in financial services

As Artificial Intelligence continues to evolve, its integration within financial services will only deepen. Institutions that successfully integrate AI into their operations will gain a significant competitive advantage. Benefiting from enhanced fraud detection, superior customer experiences, and data-driven investment strategies.

These businesses must also navigate the complexities of regulatory compliance, data privacy, and ethical AI deployment. The EU’s AI Act is one of many policies aiming to create the most robust governance structures for AI applications, and finance is no exception.

Striking the right balance between innovation and regulation will be crucial to ensuring AI remains a force for positive transformation rather than disruption. Financial institutions must prioritise transparency, human oversight, and ethical considerations in deployment to fully realise its potential while maintaining consumer trust.

The financial industry is on the brink of an AI-driven revolution. With careful implementation and responsible oversight, the technology has the power to make financial services more secure, efficient, and customer-friendly than ever before. Institutions that embrace this technology while addressing its challenges will shape the future of finance, redefining the way money is managed, invested, and protected in the years to come.

  • Artificial Intelligence in FinTech

Author

Scott Zoldi

Published

25 March 2025

Estimated Read time

4Mins

Scott Zoldi, Chief Analytics Officer at FICO, explains why there should be no AI alone in decision making processes

Many AI models are black boxes and developed without proper consideration for interpretability, ethics, or safety of outputs. To establish trust, organisations should leverage Responsible AI. This defines standards of robust AI, explainable AI, ethical AI, and auditable AI. Under Responsible AI, developers define the conditions that lead to some transactions having less human oversight and others having more. But can we take people out of the decision-making loop entirely? To answer that question, let’s look at some developments in Responsible AI.

Trust in Developing AI Models

One best practice that organisations can adopt is maintaining a corporate AI model development standard. This dictates appropriate AI algorithms and processes to enable roles that keep people in the loop. This will often include the use of interpretable AI, allowing humans to review and understand what AI has learned for palatability, bias, ethical use and safety. Auditable AI will then codify the human-in-the-loop decisions and monitoring guidelines for operational use of the AI.

Responsible AI codifies all the essential human decisions that guide how AI will be built, used and progressed. This includes approving or declining the use of data, removing unethical relationships in data (i.e., illegal or unethical data proxies), and ensuring governance and regulation standards are met. Responsible AI leverages an immutable blockchain that dictates how to monitor the AI in operation. And the decision authority of human operators, which can include conditions where AI decisions are overruled, and operations move to a ‘humble AI model.’ AI Practitioners are keenly aware that even the highest performing AI models generate large number of false positives. So, every output needs to be treated with care and strategies defined to validate, counter, and support the AI.

A Responsible AI framework

There should be a well-defined process to overrule or reverse AI-driven decisions. If built in a Responsible AI framework, these decisions are codified into a crystal-clear set of operating AI blockchain frameworks well before the AI is in production. When there is a crisis you need clear preset guidance, not panicked decision making. This blockchain will define when humans can overrule the AI through alternate models, supporting data, or investigative processes. This AI operating framework is defined in coordination with the model developers, who understand the strengths and weaknesses of the AI. And when it may be operating in ways it wasn’t designed, ensuring there is no gap between development and operation. When auditable AI is employed, there are no nail-biting decisions in times of crisis. You can rely on a framework that pre-defines steps to make these human-driven decisions.

Companies that utilise Responsible AI frameworks enforce usage adherence by auditable AI, which is the operating manual and monitoring system. Embracing Responsible AI standards can help business units attain huge value. At the same time they can appropriately define the criteria where the businesses balance business risks and regulation. Domain experts/analysts will be given a defined span of control on how to use their domain knowledge and the auditable AI will monitor the system to alert and circumvent AI as appropriate.

Drawback prevention begins with transparency

To prevent major pull-back in AI today, we must go beyond aspirational and boastful claims to honest discussions of the risks of this technology. We must define how involved humans need to be. Companies need to empower their data science leadership to define what is high-risk AI, and how they are prepared or not to meet responsible/trustworthy AI. This comes back to governance and AI regulation. Companies must focus on developing a Responsible AI programme, and boost practices that may have atrophied during the GenAI hype cycle. 

They should start with a review of how AI regulation is developing, and whether they have the tools to appropriately address and pressure-test their AI applications. If they’re not prepared, they need to understand the business impacts of potentially having AI pulled from their repository of tools. And get prepared by defining AI development/operational corporate standards. 

Companies should then determine and classify business problems best suited for traditional AI vs. generative AI. Traditional AI can be constructed and constrained to meet regulation using the right algorithms to meet business objectives. Finally, companies will want to adopt a humble AI approach to have hot backups for their AI deployments. And to tier down to safer tech when auditable AI indicates AI decisioning is not trustworthy.

The vital role of the Data Scientist

Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of AI algorithms’ mathematics and risks. Stringing together AI is easy. Building AI that is responsible and safe and properly operationalised with controls is a much harder exercise requiring standards, maturity and commitment to responsible AI. Data scientists can help businesses find the right paths to adopt the right types of AI for different business applications, regulatory compliances, and optimal consumer outcomes. In a nutshell: AI + human is the strongest solution. There should be no AI alone in decision-making.

  • Artificial Intelligence in FinTech
  • Blockchain

Author

Dan Brightmore

Published

21 March 2025

Estimated Read time

5Mins

InsurTech Insights Europe 2025: A Transformational Gathering for the Future of Insurance

InsurTech Insights Europe 2025, held on March 19-20 at the InterContinental London – the O2, reaffirmed its status as the premier conference for insurance technology professionals across the continent. Drawing more than 6,000 attendees from over 80 countries, the event brought together C-level executives, startup founders, investors, and tech leaders. They explored the evolving future of insurance powered by innovation and digital transformation.

Key Themes

With seven stages and over 400 speakers, the conference agenda was packed with compelling keynotes, forward-looking panel discussions, fireside chats, and practical workshops.

The overarching theme of the 2025 edition was crystal clear: artificial intelligence (AI) is no longer a futuristic concept, it’s the driving force behind today’s insurance innovation. Topics like automation, generative AI, claims transformation, underwriting analytics, embedded insurance, cyber security, and ESG all reflected a dynamic industry poised for rapid acceleration.

A Focus on Leadership & Diversity

One of the standout sessions was the panel discussion titled “The ROI of Gender Diversity: Breaking the Glass Ceiling for Women in Leadership”, held on the Purple Stage. Featuring high-level voices from Solera, unlock VC, and AXA XL, the panel addressed the often-overlooked yet crucial importance of gender diversity in executive roles. The discussion didn’t stop at raising awareness; it presented measurable business outcomes tied to diverse leadership and called for action to foster inclusivity across all levels of the industry.

Complementing this session was “The Women in Insurance Power Group Meet-up”, a networking event held at the Sky Bar on the 18th floor. Attendees not only connected over lunch but were also invited into an exclusive WhatsApp group, encouraging long-term collaboration and support among female leaders and allies in the space.

The Innovators Hub and the ITI Marquee: Where the Future Was Born

A major addition to this year’s conference was the debut of the ITI Marquee. A vibrant, purpose-built zone dedicated to showcasing bold ideas and startup brilliance. This space housed the Innovators Hub, which included its own dedicated Innovator’s Stage. Here, early-stage ventures and InsurTech pioneers pitched their solutions to panels of VCs, corporate innovation leads, and fellow founders.

This setting offered more than exposure, It cultivated real-time connections between startups and investors, giving many smaller players their first shot at meaningful partnerships or funding opportunities. The diversity of ideas, from AI-powered claims processors to data-driven risk models for climate insurance, reflected the industry’s hunger for next-gen solutions.

Keynote InsurTech Highlights

One of the most talked-about moments of the event came from Daniel Schreiber, CEO and Co-Founder of Lemonade, whose opening keynote explored how AI can dramatically enhance customer experience in insurance. He challenged the audience to rethink not just how insurance is sold or serviced, but why it’s offered. And how technology can transform its social impact.

Another crowd favourite was the session on “The Path to Embedded Insurance”, which unpacked how insurance products are increasingly being bundled into digital ecosystems like ecommerce platforms, mobility apps, and smart home technologies. This wasn’t just a hype piece. Real-world case studies from European neobanks and auto insurers illustrated how embedded models are already driving customer growth and retention.

Among the compelling keynotes on the Main Stage, Sofia Kyriakopoulou, a Fintech Strategy AI Champion and Group Chief Data & Analytics Officer at SCOR, revealed how GenAI innovation at one of the world’s largest reinsurers is transcending the realm of proof of concepts to become fully productive.

InsurTech Deep Dives: AI, Data & Digital Claims

Sessions throughout the week made it clear that AI is at the forefront of virtually every area of insurance operations. Whether it was applied in predictive underwriting, fraud detection, or personalised customer engagement, companies are looking to AI not just for marginal gains but foundational transformation.

A standout workshop on AI in Claims Automation included live demos from startups using computer vision and NLP to automate damage assessment. Meanwhile, a session on Data-Driven Underwriting shared how insurers are replacing traditional risk proxies with real-time data streams, from wearables to smart meters.

Cybersecurity was another hot topic, with insurers discussing how to build resilient cyber products in the face of increasing digital threats and regulatory complexity.

Global Meets Local: The Power of Diversity

Although a European event at heart, the conference had a distinctly global flair. Speakers came from the U.S., Singapore, Brazil, South Africa, and the Middle East. They brought diverse perspectives on shared challenges such as climate change, digital regulation, and consumer trust.

Simultaneously, European startups shone on stage. Companies from the UK, Nordics, DACH, and Benelux presented innovative, often niche solutions for localised market challenges—from parametric crop insurance to real-time mobility coverage.

Trade Exhibition & Brand Visibility

The exhibition floor was a hive of activity, featuring booths from established players like Munich Re, Swiss Re, Guidewire, Duck Creek, and Cognizant, alongside vibrant startup showcases. Product demos, swag giveaways, and live challenges kept engagement high and made it easy for brands to stand out.

The conference proved to be a golden opportunity for brand elevation, allowing companies to position themselves as thought leaders or rising disruptors in front of an incredibly curated audience.

InsurTech Insights Europe: The Verdict

The closing remarks from Kristoffer Lundberg, CEO of InsurTech Insights, captured the spirit of the event:

“It’s a privilege for us to gather together the sharpest minds in the industry to discuss the role of AI in insurance. The direction and impact of these technologies will shape the space for decades to come.”

Indeed, InsurTech Insights Europe 2025 wasn’t just a conference, it was a strategic gathering. A melting pot of ideas and a launchpad for the next generation of insurance products and platforms. Attendees walked away not just with new business cards, but with fresh ideas, collaborative leads, and the motivation to drive innovation within their own organisations.

As the insurance industry continues to evolve amid mounting global challenges and rapidly advancing tech, this event served as a timely and energising reminder… The future is not something to wait for—it’s something to build, together.

  • Artificial Intelligence in FinTech
  • InsurTech

Author

Dan Brightmore

Published

17 March 2025

Estimated Read time

6Mins

MoneyLIVE Summit 2025: A stellar combination of thought leadership, cutting-edge technology showcases and unparalleled networking opportunities

The MoneyLIVE Summit 2025, held on March 10th-11th at London’s Business Design Centre, once again positioned itself as one of the most significant events in the banking and financial services industry. With over 1,500 attendees, 200+ speakers, and an agenda packed with insights on digital transformation, AI-driven innovation, and payment advancements, the event delivered a comprehensive overview of the future of financial services.

As one of Europe’s most influential FinTech and banking conferences, MoneyLIVE Summit attracted executives from leading institutions, including HSBC, Revolut, Standard Chartered, Barclays, Google, and Mastercard, providing attendees with unparalleled networking opportunities and deep dives into the latest industry developments.

The 2025 edition of MoneyLIVE Summit focused on several key themes within the financial sector, including:

  • AI and Automation in Banking
  • The Future of Payments and Open Banking
  • Sustainability and ESG in Finance
  • The Evolution of Embedded Finance
  • Cybersecurity and Fraud Prevention
  • Modernising Legacy Systems

AI and Automation: The Next Frontier

One of the most anticipated discussions centredd on Artificial Intelligence (AI) and Automation in Financial Services. Keynote speakers such as Taylan Turan (CEO, Retail Banking, HSBC) and Francesca Carlesi (CEO, Revolut UK) highlighted how AI is revolutionising customer interactions, risk assessments, and fraud detection.

A standout panel featured representatives from Google Cloud, Lloyds Banking Group, and Monzo, discussing the ethical implications of AI-driven banking and how institutions can balance efficiency with regulatory compliance. The consensus? AI is no longer a futuristic concept but an operational necessity.

On the opening day we spoke with Tim Mason, Managing Director for Artificial Intelligence at Deutsche Bank, and Publicis Sapient VP Jan-Willem Weggemans, about the rise of Agentic AI. Look out for this feature in the May edition of FinTech Strategy Magazine. Publicis Sapient also hosted an AI Champions Meet Up.

The Future of Payments and Open Banking

With open banking continuing to disrupt traditional financial models, this year’s summit included multiple sessions on its evolution. Speakers from Visa, Mastercard and Stripe explored how real-time payments and digital wallets are reshaping the customer experience.

One of the most engaging sessions was on CBDCs (Central Bank Digital Currencies) and the impact of digital currencies on global trade. Representatives from the Bank of England and the European Central Bank provided valuable insights into regulatory developments and the long-term feasibility of CBDCs in mainstream banking.

Sustainability and ESG in Finance

The financial industry’s role in Environmental, Social, and Governance (ESG) initiatives was another critical theme. With growing investor interest in sustainable finance, executives from Barclays, NatWest, and BlackRock discussed how banks can integrate ESG principles into lending and investment strategies.

A major highlight was a fireside chat with Ana Botín, Executive Chairman of Santander Group, who emphasised the need for banks to take the lead in financing climate action while maintaining profitability. She stressed that FinTech innovation must align with sustainability goals to drive real change.

Notable Speakers & Thought Leadership

MoneyLIVE Summit 2025 featured an impressive lineup of speakers, including CEOs, policymakers, and FinTech pioneers. Notable names included:

  • Francesca Carlesi (CEO, Revolut UK) – Discussed the role of challenger banks in redefining customer expectations.
  • Taylan Turan (CEO, Retail Banking, HSBC) – Spoke about how traditional banks must adapt to stay competitive in an increasingly digital world.
  • Saif Malik (CEO, UK, Standard Chartered Bank) – Shared insights on the rise of embedded finance and its impact on global banking.
  • Anne Boden (Founder, Starling Bank) – Highlighted the impact of neobanks on legacy banking institutions.
  • Google Cloud & AWS Representatives – Covered AI’s growing role in fraud prevention and customer engagement.
  • Lee McNabb (Head of Payment Strategy, NatWest) – Shared views on modernising core payment architecture for the long term.

The diversity of perspectives provided attendees with a well-rounded understanding of the industry’s challenges and opportunities in the coming years.

MoneyLIVE Networking & Attendee Experience

Networking has always been a key highlight of MoneyLIVE Summit, and the 2025 edition did not disappoint. The event provided ample opportunities for professionals to connect, with dedicated networking zones, private meeting areas, and an exclusive VIP lounge for C-level executives.

The FinTech Startup Village was a must-visit area, showcasing some of the most innovative fintech startups in Europe. Several emerging companies, specializing in AI-driven financial advisory, blockchain-based payments, and RegTech solutions, presented their groundbreaking products.

A standout initiative was the Women in Finance Roundtable, which focused on fostering greater gender diversity in leadership roles within the financial industry. Featuring influential female leaders from Citi, JPMorgan, and Monzo, the discussion encouraged actionable steps towards inclusivity and representation. Publicis Sapient also hosted a networking session on Celebrating Women in Finance.

Exhibition & Innovation Showcase

The exhibition hall was bustling with activity, featuring booths from major players like IBM, Microsoft, Accenture, and Salesforce, as well as FinTech disruptors showcasing cutting-edge solutions. Attendees had the opportunity to experience hands-on product demos, including AI-powered chatbots, biometric authentication for secure banking, and blockchain-based smart contract platforms.

One of the most talked-about innovations was Quantum Computing in Financial Services, presented by IBM. Experts explored how quantum computing could enhance complex financial modelling, risk analysis, and fraud detection, potentially transforming the industry in the next decade.

Key Takeaways & Industry Impact

MoneyLIVE Summit reaffirmed its reputation as a forward-thinking, insightful event that brings together the brightest minds in finance and technology. Some of the key takeaways included:

  • AI is mainstream – Banks and fintech firms must embrace AI-driven solutions to enhance customer experience and operational efficiency.
  • Payments are evolving rapidly – With open banking, digital wallets, and real-time payments on the rise, banks need to innovate or risk being left behind.
  • Cybersecurity remains a top priority – With increased digital transactions, fraud prevention and regulatory compliance are more critical than ever.
  • Sustainability cannot be ignored – ESG-focused financial strategies are no longer optional but a necessity for long-term growth and investor confidence.
  • Embedded Finance is the future – Traditional banks and fintechs must collaborate to integrate financial services seamlessly into everyday life.

MoneyLIVE: The Verdict

MoneyLIVE Summit 2025 lived up to expectations, delivering a stellar combination of thought leadership, cutting-edge technology showcases and unparalleled networking opportunities. For professionals in banking, payments, fintech, or regulatory compliance, this event provided invaluable insights into the industry’s trajectory.

The only potential downside? With so many high-quality sessions running simultaneously, attendees had to make tough choices about which discussions to prioritise. However, the availability of on-demand session recordings meant that all the key insights attendees need were available.

With an impressive lineup of speakers, a strong focus on industry trends, and excellent networking opportunities, MoneyLIVE Summit remains a must-attend event for financial professionals looking to stay ahead in an ever-evolving landscape.

  • Artificial Intelligence in FinTech
  • Digital Payments
  • Embedded Finance

Author

Dan Brightmore

Published

13 March 2025

Estimated Read time

5Mins

Fouzi Husaini, Chief Technology & AI Officer at Marqeta, answers our questions about Agentic AI and its applications for businesses

Agentic AI is emerging as the leading AI trend of 2025. Industry figures are hailing Agentic AI as the broadly transformative next step in GenAI development. The year so far has seen multiple businesses release new tools for a wide array of applications. 

The technology combines the next generation of AI tech like large language models (LLMs) with more traditional capabilities like machine learning, automation, and enterprise orchestration. The end result could lead to a more autonomous version of AI: Agents. These agents can set their own goals, analyse data sets, and act with less human oversight than previous tools. 

We spoke to Fouzi Husaini, Chief Technology & AI Officer at Marqeta about what sets Agentic AI apart whether the technology really is a leap forward in terms of solving AI’s shortcomings, and how Agentic AI could solve business problems.

1. What makes AI “agentic”? How is the technology different from something like Chat-GPT? 

“Agentic refers to the type of Artificial Intelligence that can act as agents and on its own. Agentic AI leverages enhanced reasoning capabilities to solve problems without prompts or constant human supervision. It can carry out complex, multi-step tasks autonomously.

“GenAI and by extension Large Language Models, the most famous example being ChatGPT, require human input to solve tasks. For instance, ChatGPT needs user prompts before it can generate content. Then, sers need to input subsequent commands to edit and refine this. Agentic AI has the capability to react and learn without human intervention as it processes data and solves problems. This enables it to adapt and learn much faster than GenAI.”

2. Chat-GPT and other LLMs frequently produce results filled with factual errors, misrepresentations, and “hallucinations”, making them pretty unsuited to working without human supervision – let alone orchestrating important financial deals. What makes Agentic AI any better or more trustworthy? 

“All types of AI have the possibility to ‘hallucinate’ and produce factually incorrect information. That being said, Agentic AI is usually less likely to suffer from significant hallucinations in comparison to GenAI. 

“Agentic AI’s focus is specifically engineered to operate within clearly defined parameters and follow explicit workflows, making it particularly well-suited for having guardrails in place to keep it on task and from making errors. Its learning capabilities also allow it to recognise and adapt to its mistakes, ensuring it is unlikely to hallucinate multiple times.”

“On the other hand, GenAI occasionally generates factually incorrect content due to the quality of data provided, and sometimes because of mistakes in pattern recognition.”

“In fintech, Agentic AI technology can make it possible to analyse consumer spending data and learn from it, allowing for highly tailored financial offers and services that are more accurate and help to create a personalised finance experience for consumers.” 

3. How could agentic AI deployments affect the relationship between financial services companies and their customers? What about their employees? 

“The integration of Agentic AI into financial services benefits multiple parties. First, 

integrating Agentic AI into their offerings allows financial service companies to provide their customers with bespoke tools and features. For instance, AI can be used to develop ‘predictive cards’. These cards can anticipate a consumer’s spending requirements based on their past behaviour. This means AI can adjust credit limits and offer tailored rewards automatically, creating a personalised experience for each individual.

“The status quo’s days are numbered as consumers crave tailor-made financial experiences. Agentic AI can allow fintechs to provide personalised financial services that help consumers and businesses make their money work better for them. With Agentic AI technology, fintechs can analyse consumer spending data and learn from it. This allows for more tailored financial offers and services.   

“As for employees, Agentic AI gives them the ability to focus on more creative and interesting tasks. Agentic AI can handle more routine roles such as data entry and monitoring for fraud, automating repetitive tasks and autonomous decision making based on data. This helps to reduce human error and enables employees to focus more time and energy on the creative and strategic aspects of their roles while allowing AI to focus on more administrative tasks.”

4. How would agentic AI make financial services safer? 

“Agentic AI has the capability to make financial services more secure for financial institutions and consumers alike, by bringing consistency and tireless vigilance to critical financial processes. With its ability to analyse vast strings of information, it can rapidly identify anomalies in spending data that indicate potential instances of fraud and can use its enhanced reasoning and ability to act without human prompts to quickly react to suspicious activity. 

“While a human operator will be susceptible to decision fatigue, an AI agent could always be vigilant and maintain the same high level of precision and alertness 24/7. This is vital for fields like fraud detection, where a single missed signal could lead to significant consequences.

“Furthermore, its capability to learn without human interaction means that it can improve its ability to detect fraud over time. This gives it the ability to learn how to identify new types of fraud, helping it to adapt as schemes become more sophisticated over time.” 

5. What kind of trajectory do you see the technology having over the next year to eighteen months?

“In fintech, Agentic AI integration will likely begin in the operations space. These areas manage complex, but well-defined, processes and are perfect for intelligent automation. For instance, customer call centres where human agents usually follow set standard operating procedures (SOPs) that can be fed into an AI system, which makes automation easier and faster than before.

“In the more distant future, I believe we will see Agentic AI integrated into automated workflows that span entire value chains, including tasks such as risk assessment, customer onboarding and account management.” 

  • Artificial Intelligence in FinTech

Author

Stuart Cheetham

Published

22 January 2025

Estimated Read time

4Mins

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

Author

Simon Ellis

Published

13 January 2025

Estimated Read time

4Mins

Simon Ellis, Head of Operations, EMEA at the global payment platform Airwallex, with his key FinTech predictions for 2025

This past year has truly solidified FinTech’s role as an indispensable part of the financial landscape. From the sleek banking apps that have become a daily staple for millions to the invisible financial infrastructure powering seamless payments… FinTech is no longer just a buzzword – it’s the new norm. It’s not just about innovative startups anymore. It’s about how traditional financial institutions are embracing and integrating these technologies to stay relevant and meet the evolving needs of their customers. In essence, FinTech has become synonymous with modern banking, driving continuous innovation and transformation across the entire industry.

But what’s next? 2025 promises to be transformational in many ways. Businesses are accepting AI as the reality of our future. Consumers are becoming more accustomed to flexibility and choice. And now more than ever, both seek agile and seamless solutions for their financial needs.

Here are the trends we at Airwallex expect to see play out over the next twelve months:

Coopetition with traditional banks and FinTechs

Coopetition will become a firm reality in 2025. Many of the conventional fears associated with FinTechs will no longer be a salient presence. Increased collaboration between FinTechs and banks will facilitate further innovation on a mass scale. These giants will insert themselves into new industries and access a new generation of customers. The prerequisite for this is the correct compliance and controls but firms are committed to getting this right to ensure they maintain the right to operate.

A path to Hyper-Personalisation

Over the past year, we’ve witnessed the emergence of an increasingly educated payments buyer, mindful of what they need today and into the future as they scale. In 2025, we’ll see payment providers take more direction from their customers. Payment providers will need to be more flexible as merchant customer preferences shift.

For ambitious and progressive businesses, particularly those looking to operationalise in new markets at pace, convenience remains king. Navigating multiple vendors can quickly create friction. However, this is where FinTechs bundling their solutions comes into play. Having a single point of contact that provides a range of services – whether that’s foreign exchange (FX), multi-currency digital accounts, expense management or payouts – isn’t just about convenience. Moreover, it saves on crucial costs that can be reinvested back into the business to spur growth.

The days of taking a one-size-fits-all product approach will no longer suffice. Hyper-Personalisation will reign supreme as businesses fight for customer attention and brand loyalty.

The continued rise of Embedded Finance

In seeking to accommodate the growing need for flexibility, the year ahead will see Embedded Finance become more commonplace across a range of industries. Consumers have most likely all experienced the seamless process of making an embedded payment, whether it’s ordering through a food delivery app or paying for a ride. Usually, it’s through digital-first services, but 2025 will be the year that we see more traditional industries embrace embedded payments to keep pace with the broader innovation taking place in their sectors. This will also help businesses stand out in an ever-changing and overcrowded market. Embedded Finance will help end users maintain their dominance while also driving product stickiness.

AI is here to stay

To accelerate this transformation, 2025 will see FinTechs and banks persist in the use of AI to improve decision-making and customer engagement. AI is already being used to automate and expedite previously long and complex processes. For example, Generative AI (GenAI) will continue to help financial institutions enhance the speed and efficacy of know-your-customer (KYC) and customer onboarding processes. Furthermore, at the same time, it will detect unusual activity and fraud. We’ll also continue to see a focus on AI’s use to improve the ongoing customer experience with personalised insights and advice.

As a result, modern financial services will become more accessible to businesses of all sizes and across sectors. Allowing for easier and more cost-effective management of global operations.

  • Artificial Intelligence in FinTech
  • Digital Payments
  • Embedded Finance

Author

Fernando Henrique Silva

Published

10 January 2025

Estimated Read time

5Mins

Fernando Henrique Silva, SVP Digital Solutions, EMEA at CI&T, on how finance firms can best leverage AI to unlock bespoke services and rapid issue resolution

When OpenAI released ChatGPT in November 2022, businesses in banking and finance quickly recognised the commercial potential of Generative AI (GenAI). However, due to the AI technology’s nascent qualities, archaic legacy systems and a lack of established strategies for integration, leaders have struggled to translate GenAI into greater revenues.

Two years on, the landscape is finally taking shape. According to PwC, 70% of global CEOs now expect GenAI to significantly reshape how their operations create value. Furthermore, more than two-thirds are already working with AI, having reworked their tech strategies to align with AI-driven opportunities.

Of course, the banking and finance sector is no stranger to technological change. The first plastic credit card was introduced in 1959, by American Express. The ATM was launched in London by Barclays Bank. And today, mobile banking, investing and high-level financial management can be done by any smart device nestled in a person’s pocket.

However, as with any new frontier tech, GenAI has its risks: implementation challenges, upskilling, regulatory and ethical considerations—these risks are heightened in finance and banking. And there’s the classic possibility of simply getting it wrong. Plus, what’s hot in GenAI today may be old news tomorrow.

To help organisations drive change within, let’s explore the good, the bad, and the ugly of GenAI adoption through the lens of recent insights from CI&T research and case studies.

The Good side of GenAI

The analogy between the Old West and GenAI holds up: both involve exploring new territories, uncovering valuable resources, and building infrastructure. Today, these frontier outposts are becoming cities, and full-scale reinvention is on the horizon for financial institutions.

So, what’s the new gold rush? According to CI&T’s new research, The Future of Finance: How AI is powering the intelligence era, the answer is ‘hyper-personalisation.’ This field is ripe, with fintech firms using it to deliver two key benefits: bespoke services and rapid issue resolution.

Using Customer Data Profile software—tools that gather and standardise data from online and offline sources to create detailed customer profiles—GenAI is helping these firms take personalisation to new depths. This can enable bespoke services in real-time. Indeed, McKinsey reports that personalisation drives profit: companies that prioritise it achieve growth rates 40% higher than their peers. For example, it enables institutions to offer solutions that foster smarter money habits among customers. This can be done by aligning services with consumption patterns and inflationary trends. This strengthens customer loyalty while driving cross-selling opportunities. Similarly, by facilitating enhanced financial decision-making, financial institutions can provide tailored advice and tools that differentiate their services in a competitive market, boosting retention rates.

On the investment side, hyper-personalisation creates avenues for smart investment moves by delivering customised strategies aligned with individual risk profiles. This not only attracts more customers but also improves portfolio performance, translating into increased asset management fees and long-term profitability.

GenAI is also giving businesses the gift of time. By 2030, up to 30% of current hours worked could be automated. For example, in the financial sector, portfolio managers are using GenAI to automate routine performance and risk reports. Hyper-personalisation could lead to strategies tailored to individual risk appetites, the latter being a revenue opportunity.

The Bad with GenAI

GenAI is like the newest member of the crew, full of promise but with some questionable traits. Without oversight, it can enable manipulation, misinformation, and privacy breaches. The tech, unmanaged, can also be prone to biases and inaccuracies. Often presenting errors as facts, adding pressure on teams to manage them. Moreover, it poses a security risk, requiring businesses to safeguard their data, or risk being ‘robbed in the night.’

To manage these risks, GenAI is increasingly subject to complex regulations. Gartner predicts that by 2026, 50% of governments will introduce regulations and policies to enforce the responsible use of AI. These challenges will be even more significant in banking and finance.

Balancing the pros and cons of GenAI is the key to extracting value. GenAI itself can often help. For example, CI&T assisted fintech firm Bulla, which specialises in flexible credit and benefits, with managing common complaints. Using our enterprise-ready GenAI platform, CI&T FLOW, Bulla analysed customer service data to gain a detailed view of pain points and rethink support systems. They also used it to give employees access to essential information and to train staff in GenAI.

The Ugly side of Artificial Intelligence

When the going gets tough, our relationship with GenAI can take an ugly turn if outdated legacy systems stand in the way. The challenge of digging through impenetrable layers, reworking outdated processes, extracting valuable data, and training staff accustomed to old ways of working is no easy feat. Moreover, the costs can quickly add up.

Historically, banking has been one of the sectors worst affected by legacy hardware. Nearly six in ten bankers see their legacy systems as a major business challenge, describing them as a ‘spaghetti junction’ of interconnected but antiquated technologies. So, much like digging through rock in search of gold, the rigid hardware architectures designed for specific banking functions—based on old programming languages and databases—are holding back innovation. In fact, 60% of executives cite skills gaps as an obstacle to overcome in their digital transformations.

The banking sector may be on the brink of a breakthrough. We’re starting to see more AI-driven chatbots, fraud prevention, and the speeding up of time-consuming tasks such as developing code and summarising reports. However, it’s updating the legacy hardware where the real commercial value lies.

Ironically, GenAI holds the key. For one of CI&T’s leading clients, a large global bank based in South America, CI&T FLOW was able to modernise its systems by supporting the transition from COBOL to Python using a code refiner. This resulted in accelerated developer delivery, a 54% lead time reduction, and a 33% improvement in user story quality. Highlighting the power of strategically harnessing the technology. The challenge is also the solution.

As the world of GenAI transitions from Wild West to civilised modernity, businesses are going to have to get smart about how they look for commercial value. Often, the solution lies in GenAI itself. So, get started, and get started now. And in the immortal words of Clint Eastwood’s Blondie: “Two hundred thousand dollars is a lot of money. We’re gonna have to earn it.”

To learn more about how CI&T can help your business commercialise GenAI, download The Future of Finance: How AI is powering the intelligence era here.

  • Artificial Intelligence in FinTech

Author

Dan Brightmore

Published

3 January 2025

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

17 December 2024

Estimated Read time

2Mins

FICO’s use of Blockchain for AI model governance wins Tech of the Future: Blockchain and Tokenisation award

Global analytics software leader FICO has won the Tech of the Future – Blockchain and Tokenisation award. The Banking Tech Awards in London recognised FICO for its innovative work using Blockchain technology for AI model governance. FICO’s use of blockchain to advance responsible AI is the first time blockchain has been used to track end-to-end provenance of a machine learning model. This approach can help meet responsible AI and regulatory requirements.

More information: https://www.fico.com/blogs/how-use-blockchain-build-responsible-ai-award-winning-approach-0

FICO: Blockchain Innovation

FICO’s AI Innovation and Development team has developed and patented an immutable blockchain ledger. It tracks end-to-end provenance of the development, operationalisation and monitoring of machine learning models. The technology enforces the use of a corporate-wide responsible AI model development standard by organisations. It demonstrates adherence to the standard with specific requirements, people, results, testing, approvals and revisions. In addition to the Banking Tech award, Global Finance recognised FICO’s blockchain for AI technology with The Innovators award last year.

Responsible AI

“The rapid growth of AI use has made Responsible AI an imperative,” commented Dr. Scott Zoldi, chief analytics officer at FICO. “FICO is focused on technologies that ensure AI is used in an ethical way, and governance is absolutely critical. We are proud to receive another award for our groundbreaking work in this area.”

FICO is well-known as a leader in AI for financial services. Its FICO® Falcon® Fraud Manager solution, launched in 1992, was the first fraud solution to use neural networks. Today it manages some four billion payment cards worldwide. FICO has built advanced analytics capabilities into FICO® Platform, an applied intelligence platform for building decision management solutions.

See the full list of Banking Tech Award winners for 2024.

  • Artificial Intelligence in FinTech
  • Blockchain

Author

Adam Zoucha

Published

13 December 2024

Estimated Read time

4Mins

Adam Zoucha, MD EMEA at FloQast, on how businesses will modernise financial processes in 2025

With 45% of accountancy firms and in-house finance teams facing talent shortages, 2025 is going to be a critical year for many. Financial transformation is going to be the watchword. The conditions companies are facing will push them to speed up the transformation of their operations, modernising their financial processes while strengthening their company culture and vision.

The year ahead will likely see a continuation of the current period of instability, posing serious challenges for accounting teams looking to grow their business. The impact of global geopolitics is hard to predict which, twinned with the UK economy’s persistently slow growth rate, means companies will need to innovate to succeed – embracing automation, AI, and cutting-edge compliance processes.

It’s not all about the macro trends, though. On an individual level, our research this year has shown that employees are feeling the strain, and business leaders will need to take that seriously in 2025. The talent shortage is a vicious cycle – the harder it is for companies to find and retain talent, the more pressure remaining team members end up having to shoulder. The right technology can play a crucial role in reducing that stress and breaking the cycle.

Alongside those real challenges, there are real opportunities. The accounting business is changing fast, and it’s a great time to be in the industry. As we draw 2024 to a close, here are five key things accounting firms can expect to see in the new year.

Financial Transformation moving up the agenda

We’ve already looked at some of the reasons why financial transformation is going to be critical in 2025, but that doesn’t mean every CFO and accountant in the business is rushing to deliver. Based on our research  60% of accountants and CFOs still do not consider it a top priority – mainly because most don’t truly know what it means for their business, so education is key.

In essence, companies should aim to align their finance functions more closely with their organisational goals, enabling accountants to bring their expertise and insight to the decision-making process. As the finance function’s strategic role grows, there will be an urgent need for agile, digital tools that enhance collaboration and efficiency. For CFOs, embracing this transformation is essential to navigate new complexities with precision and effectiveness.

Accountancy teams will embrace new tools for the future

The talent gap present in the industry is unlikely to change any time soon. It takes time to train people, and accounting has a bit of a PR problem – its status as a secure, skilled job is battling with perceptions of stress and burnout.

As a result, in 2025, leaders will increasingly look to keep accountants motivated, engaged, and fulfilled as the declining population of new candidates continues to heap pressure on accounting teams—a trend that’s unlikely to reverse anytime soon. 

It’s essential that business leaders retain their finance professionals by fostering a fulfilling work environment. They can help by upskilling accountants and adopting technologies to reduce mundane and repetitive tasks. CFOs can play a key role by equipping their teams with future-focused skills, blending technology with strategic insight to drive real value within their organisations.

AI will power Tansformation in 2025

Transformation in 2025 won’t be limited to removing internal silos and improving staff retention, crucial though those things are. We’re also going to see AI helping accountants become key players in driving business success. The real value of AI will become apparent this year. For finance teams, it will act as a copilot, automating routine tasks and giving time back to accountants to become strategic assets for their organisations.  

This shift will help the industry tackle talent shortages with agility, turning challenges into opportunities for growth. Embracing AI isn’t just about keeping pace; it’s about unlocking accountants’ full potential as key players in driving business success.

Compliance will become a value-generating asset rather than a tick-box exercise

Compliance and risk, when managed properly, can drive real value for organisations. In 2025, the nuanced relationship between compliance, reputation, and risk means it’s likely to move up the corporate agenda. 

Technology can be a real driver here, and compliance strategies are fundamental to the larger accounting transformation journey. By taking a more holistic approach to compliance, rather than treating it as a mere check-box exercise, compliance can become a valuable asset. Currently, only 16% of organisations take this strategic view, revealing a significant opportunity for those willing to innovate and elevate their compliance efforts.

Overall, accounting businesses may be facing rough seas, but with the right tools and investments in place, they can unlock new value in 2025: transforming financial processes, improving employee satisfaction, and stepping further into their growing role as strategic advisors.

  • Artificial Intelligence in FinTech
  • Digital Payments

Author

Paul O'Sullivan

Published

11 December 2024

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

10 December 2024

Estimated Read time

5Mins

Join FinTech’s greatest event when Money20/20 Europe returns to Amsterdam’s RAI Arena June 3-5 2025

FinTech Strategy is proud to be a media partner for Money20/20 Europe 2025.

Launched by industry insiders in 2011, Money20/20 is the heartbeat of the global fintech ecosystem. Some of the most innovative, fast-moving ideas and companies have found their feet (and funding) on its show floor. From J.P. Morgan, Stripe, and Airwallex to HSBC, Deutsche Bank, and Checkout.com.

Furthermore, this is where you’ll find new connections, business-critical insights from inspirational speakers, innovation, and partnerships you need to ensure your business succeeds for whatever comes next in money.

Why Money20/20?

FinTech Strategy spoke with a host of leaders from across the FinTech spectrum. They all agreed on one thing, Money20/20 Europe is ‘the’ place to make connections and build your business.

Gurdeep Singh Kohli, Founder, SC Ventures

“It’s the first time I’ve attended Money 20/20 and, we’ve had some fascinating impromptu conversations that will lead to great opportunities. All the big names are here and it’s clearly a popular event from a thematic perspective – payments is a big theme this year. I have a very high regard for the quality of what’s on offer and the way the event has been organised – it’s a great customer experience, the way it’s all been structured, at scale, is actually one of the best I’ve ever seen. The response has been fantastic…”

Stephen Everett, MD Payables & Receivables, Lloyds Banking Group

“The majority of people at Money20/20 genuinely get up in the morning with a growth and innovation mindset. Therefore, you have to balance and recognise that when you walk into this big venue that there will be some wacky ideas. From my experience, I have seen many infant ideas turn into successful ventures, whereas I have also seen some ventures becoming unsuccessful despite having great innovation ideas. Fintechs will fail. Innovation will fail. Experiments will fail. And that’s fine. That’s what Money20/20 is all about.”

Michelle Prance, CEO, Mettle (NatWest Group)

“It’s good for Mettle to come here because we are a fintech that was incubated inside a large bank (NatWest) for fintechs. Quite often their route to market, route to capitalisation, is by going into a main bank being acquired. So, it’s that marriage between a big organisation and the small nimble fintech. People are really interested in what we’re doing because big incumbents want to be fast and nimble. They don’t always have the capital to invest in something like we’ve been able to do with Mettle. So, they’re interested to know the right route to go down. Do they incubate in house? Or do they buy it in? And what’s the right way to do that without killing the culture? These are the types of interesting conversations we’ve been having here.”

Ryan O’Holleran, Head of Sales, 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, Stripe… And then you have the Heads of Payments from companies like Booking.com, Minted and Summit who are coming here with their team 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 with that would normally take months to arrange. So, the ROI from this week is really powerful just from being able to have these conversations.”

Merusha Naidu, Global Head of Payments, Paymentology

“Paymentology is homegrown out of the UK so it’s important for us to make sure we’re representing the business across Europe. This is the centre of the world for banking innovation. We have customers here from Singapore, Dubai, Saudi Arabia, Ghana and beyond. People look to this event to really learn about what’s happening in the industry globally and discover what trends are going to come up. What should we be doing? How can we innovate together and learn from each other? That’s one of the things I really love about Money20/20; the talks in all of the panels are so interesting and I always leave knowing more. Being in the payments industry, and especially being an issue processor, it’s important for us to learn from the industry and understand where we need to move so that we can stay at the forefront of developments.”

Zak Lambert, Product Lead & Europe Lead, Plaid                                                                            

“This is my sixth straight Money20/20 and it gets busier every year! It’s great to learn more about the ecosystem at large. You can see developing trends each year, and it’s always a little bit different. You build relationships at Money20/20 that stay with you for the rest of your life. And it’s a perfect opportunity to meet people in the flesh that you might normally only see on screen. You can get a pretty direct read on what they’re working on and it’s exciting to be here making new connections.”

Book Your Money20/20 Europe Pass Now

To get a flavour of what you can expect from next year’s conference check out our review of Money20/20 Europe 2024.

Book your pass now and save €200 with the code FTS200.

  • Artificial Intelligence in FinTech
  • Digital Payments
  • InsurTech
  • Neobanking

Author

Jamil Jiva

Published

19 November 2024

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

15 November 2024

Estimated Read time

2Mins

FinTech Connect shapes the future of financial services with the UK’s only full FinTech ecosystem event at London’s Excel December 4-5

Join us as FinTech Connect welcomes world leading Fintechs, Financial Institutions, Challenger Banks, Merchants, Scale-Ups and StartUps, Investors, Accelerators and Media to The ExceL, London. 

FinTech Connect

Each year we welcome visionaries from the UK, Europe and beyond all looking to innovate within the market, expand their footprint and drive businesses forward. The event brings all this under one roof, over two insight-packed days, sparking ideas, forging partnerships and accelerating change. 

Tackling the hottest topics and biggest challenges in the fintech market. Including: embedded finance, Web3, cross-border payments, investment, scaling, Gen AI, crypto, regulation, digital innovation and customer experience (CX).

Our mission is to connect the global thought leaders across the FinTech ecosystem in an event like no other. Set yourself up for a strong 2025 by signing up for the UK’s only full FinTech ecosystem event and join 2,000+ fintech leaders in London.

Insights from FinTech’s biggest names

We’ll be asking the big questions… What AI elements do financial institutions need to follow? Build, buy or partner? What opportunity works best in the modern ecosystem? How are banks advancing their digital transformations in 2024? Who owns the CX?

Gain insights on these topics and more from some of the biggest names in financial services. Speakers include Victoria Cleland, Executive Director – Payments, Bank of England; Rory Tanner, Head of UK Government Affairs at Revolut and Nick Kerrigan, Managing Director, Swift. Thought leaders will also be taking to the stage from HSBC, DZ Bank, Lloyds Banking Group, BT and a host of other leading institutions.

Keep up to date with the latest speakers, discussions and more. Download the full agenda here.

Book your place now!

Visit Fintech Connect to book your place at The Excel now.

For a 20% discount use the code: FS20

The Global FinTech Ecosystem. Connected.

  • Artificial Intelligence in FinTech
  • Neobanking

Author

Scott Zoldi

Published

31 October 2024

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

30 October 2024

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

24 October 2024

Estimated Read time

4Mins

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

Author

Gabe Hopkins

Published

16 October 2024

Estimated Read time

6Mins

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

Author

Sejal Mehta, Andrew Rodgers & Randy Bean

Published

11 October 2024

Estimated Read time

5Mins

Sejal Mehta and Andrew Rodgers from Odgers Berndtson’s Global FinTech Centre of Excellence and Randy Bean, a Senior Advisor to Odgers Berndtson and industry author, explore the dynamics shaping leadership in the UK fintech sector

The UK FinTech sector is undergoing a significant transformation, marked by maturation, consolidation, and a more selective investment landscape. Funding is increasingly funnelled towards profit-generating scale-ups, and away from newer entrants.  

At the same time, the sector is shaped by a multi-generational workforce with varied perspectives. Meanwhile rapid advancements in AI foster apprehension and excitement. These converging factors make FinTech one of the most dynamic and competitive spaces to work in today. This presents both challenges and opportunities for its leaders.

From our perspective as global FinTech executive search and leadership advisors at Odgers Berndtson these shifts are reshaping the demands placed on leadership. They are also influencing what it takes to lead effectively in this fast-changing sector. Here, we explore the leadership trends that are emerging as a result.

Ethical FinTech leadership

Venture capital funding is now more selective and private equity investors are increasingly targeting fintechs with solid exposure. This is creating a difficult environment for new start-ups. Those attracting funding are typically cash-positive scale-ups.

Amidst these challenges, more FinTech firms are opting to list on the NASDAQ rather than the London Stock Exchange, as the UK navigates more stringent regulation. The need for payments licences, extensive reporting, and compliance demands weigh heavily on FinTech leaders.

In this landscape, we’re seeing leaders with experience in regulated financial services bring a valuable skillset. The ability to operate within defined regulatory frameworks while generating growth. FinTech boards are looking for leaders with high authenticity and who can make ethical decisions. And while balancing ambition and growth with the realities of working in a highly regulated space.

Founder replacements

We are in the midst of the FinTech sector’s maturation. Start-ups are transitioning into scale-ups, requiring different leadership competencies. For many, this requires the founder to step down or step into a board role and appoint a CEO who can take the business through its next stage of growth.

This requires leaders who are commercially driven, capable of shaping market strategies, and adept at understanding customer needs and product-market fit. Navigating risk and regulation becomes crucial, while the founder’s creative, opportunity-led approach typically no longer dominates the new operational and strategic demands.

Boards and investors are looking for CEOs with a broader skillset and deep regulatory expertise. These leaders must also be able to attract and retain the type of talent that can sustain growth and innovation, while maintaining the ‘DNA’ that made the business so attractive in the first place.

A multi-generational workforce

Intergenerational divides are becoming more pronounced for all businesses and noticeably in sectors like FinTech. Here, younger generations with fresh perspectives are working alongside older, more experienced professionals – often from traditional financial services backgrounds.

This diversity in age, experience, and approach can be a powerful asset, but only if integrated effectively. Typically, Gen Z and Millennials prioritise flexibility, technological integration and experimentation. Meanwhile, Boomers bring valuable expertise in regulatory environments and operational effectiveness, but may be more accustomed to traditional structures and leadership styles.

Increasingly, we see FinTech leaders attempt to bridge these divides by emphasising open communication, promoting mentorship opportunities, and encouraging cross-generational collaboration. With less funding and more regulation, FinTech leaders recognise the need to identify and capitalise on the strengths of a multigenerational workforce if they are to succeed.

Leadership team dynamics

As FinTech companies scale, leadership is no longer just about the capabilities of individual leaders but about the dynamics of the entire executive team. Successful scale-ups understand the importance of assembling a leadership team that brings a diverse mix of skills, and generational perspectives to the table.

We are starting to see FinTech companies think about leadership team dynamics as they scale up. Boards are looking for a blend of strategic, operational and ethical considerations. As well as how well team members work together. Do they solve problems cohesively? Are there any unresolved tensions or conflict? Are they aligned and equipped to collectively deliver on the leadership mandate?

Many leadership teams are not optimising their potential due to misalignment of strengths. For example, we recently worked with a FinTech creating an executive team profile to identify the leadership competencies needed to deliver their mandate. This exercise enabled the team to reallocate executive responsibilities for strategic initiatives based on the required strengths, regardless of traditional job roles.

Polarising views on Gen AI

Leading organisations are experiencing a transformational moment due to accelerated interest in AI and Generative AI. 89.6% are increasing their investments in AI, while 64.2% of companies have indicated that AI will be the most transformational technology in a generation. In response, organisations are hiring for the data and AI leadership roles required to prepare their companies for an AI future.

However, this integration of Gen AI has sparked both excitement and nervousness, particularly around issues of data protection and privacy. Generational differences are especially noticeable. Younger professionals are often less concerned about data privacy, while older generations remain cautious about the security implications.

This divergence in attitudes can create tension within the organisation, as leaders grapple with how best to leverage Gen AI while ensuring compliance with stringent data protection regulations. For some FinTechs, AI is seen as a specialised area requiring dedicated focus. Meanwhile, others believe AI represents a fundamental shift in how business can be conducted and AI strategy should be woven into the fabric of every leader’s responsibilities.

This divide in attitudes reflects the broader challenges we see FinTech companies face in incorporating AI. Leaders must now navigate the balance between embracing innovation and safeguarding sensitive information. They must also ensure AI is not seen as a siloed function. It must be an integral part of their commercial and strategic vision. Given the fundamental changes in the sector, the emphasis on leadership capabilities is changing for both the individual and executive team.

  • Artificial Intelligence in FinTech
  • InsurTech

Author

Dan Brightmore

Published

8 October 2024

Estimated Read time

5Mins

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

Author

Hugo Farinha

Published

2 October 2024

Estimated Read time

5Mins

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

Author

Cullen Zandstra

Published

27 September 2024

Estimated Read time

4Mins

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

Author

Russ Rawlings

Published

25 September 2024

Estimated Read time

4Mins

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

Author

Pat Bermingham

Published

16 September 2024

Estimated Read time

8Mins

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

Author

Michael Donnelly

Published

12 September 2024

Estimated Read time

3Mins

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

Author

Dan Brightmore

Published

4 September 2024

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

27 August 2024

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

23 August 2024

Estimated Read time

3Mins

McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption…

McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption of the technology is helping the consulting titan and its peers boost revenue, across industries like Insurtech, following a period of tumult.

About 40 per cent of the New York-based firm’s client projects involve the technology. The number of AI-related customers in the past 12 months is approaching 500, Rodney Zemmel, senior partner and head of the firm’s digital business, said in an interview.

“We believe the long- or the medium-term economic implications are very real,” Zemmel said. He was a final candidate in the recent global managing partner leadership elections at the firm. According to people familiar with the matter, who asked not to be identified discussing confidential information.

Though there’s some degree of hype around AI, “we’re seeing the organisations that are doing that are getting value from it,” Zemmel said. “It’ll be a little longer, and maybe, a little harder than people think, but we’ve got no doubt that the value is there,” he added.

AI adoption across Insurtech

Among those deploying automation rapidly are the traditional and regulated industries such as banking and insurance, Zemmel said. In a June report, Citigroup Inc. said AI is poised to upend consumer finance and make workers more productive. Additionally, with a high potential for 54 per cent of jobs across banking to be automated. Citi also said that the technology could add $170 billion to the industry’s coffers by 2028.

JPMorgan Chase & Co. Chief Executive Officer Jamie Dimon has called AI “critical” to his company’s future success. He also noted the technology can be used to help the firm develop new products, drive customer engagement, improve productivity and enhance risk management.

The surge in automation has come as a relief for the broader consulting industry. It has been battling a slowdown in demand for its traditional services. McKinsey, Ernst & Young and PricewaterhouseCoopers have been cutting jobs to weather the slump. Furthermore, Accenture Plc shares tumbled in March after the company warned it’s seen financial-services customers, including Insurtech, rein in spending on its software.

AI’s rise is also diverting some budgets toward specialist consultancies. Although AI-focused units like McKinsey’s QuantumBlack are growing rapidly, according to Zemmel.

McKinsey – QuantumBlack

McKinsey, which has advised everyone from the U.S.’ Pentagon to China’s Ping An Insurance Group Co., currently has about 2,000 people working across QuantumBlack. It has 7,000 staff in total in tech-related fields, according to Zemmel’s estimates. McKinsey’s headcount stood at about 45,000 globally as of 2023 and revenues were at a record $16 billion.

Zemmel said that the firm is still evaluating how the use of AI will impact its own headcount over the longer run. McKinsey had earlier warned about 3,000 of its consultants that their performance was unsatisfactory and will need to improve.

“We’re certainly planning on being agile about it,” Zemmel said. “One thing that’s clear is everybody in our organization’s going to need to know how to use AI and incorporate in their day-to-day work if they’re going to remain relevant to their clients.”

  • Artificial Intelligence in FinTech
  • InsurTech

Author

Harry Menear

Published

15 August 2024

Estimated Read time

3Mins

AI-powered threat detection, automation, and data analysis are empowering fintech cybersecurity teams to more effectively meet the challenges of an evolving world.

Artificial intelligence (AI) is driving a new generation of modern cybersecurity solutions. The technology is transforming how organisations protect against evolving digital threats, as predictive and big data analytics bring new benefits to the sector. 

How is AI transforming cybersecurity for fintech teams? 

AI’s importance in cybersecurity lies in its ability to provide advanced threat detection, automate responses, and adapt to evolving threats. It can also handle large amounts of data, making monitoring networks and detecting issues easier without increasing risks. 

AI learns from past experiences, recognising patterns and improving over time. This makes it good at spotting weak passwords and alerting the right people. AI can also block harmful bots that try to overload websites. AI automates large amounts of tasks, allowing for 24/7 monitoring and quicker responses to security threats.

Its machine learning algorithms analyse vast datasets in real-time, identifying patterns and anomalies to detect emerging threats. As AI excels in behavioural analytics, it establishes a baseline of normal behaviour to spot deviations that indicate security threats. 

Unlike traditional methods that rely on predefined signatures, AI can identify zero-day threats—new and previously unknown vulnerabilities—promptly. This proactive approach allows organisations to respond swiftly, preventing potential breaches before they occur.

AI also enhances threat intelligence by automating the analysis of code and network traffic, freeing up human analysts for more complex tasks. It, in turn, facilitates automated incident responses, rapidly mitigating attacks and minimising damage.

Predictive AI in Fraud Detection

AI is revolutionising fraud prevention by using predictive and behavioural analysis to detect and prevent fraudulent activities. By analysing historical data, AI identifies patterns that often precede fraud. This approach not only enhances detection accuracy but also reduces false alarms, distinguishing between normal and suspicious behaviours with greater precision.

In real-time, AI monitors multiple transactions simultaneously, flagging suspicious activities as they happen to mitigate risks promptly. It learns individual customer behaviours to detect anomalies, such as large transactions or unusual patterns. These triggers prompt alerts for investigation or automated protective measures, such as account freezing.

Despite challenges such as data privacy and the need for extensive datasets, AI’s advancements in machine learning promise increasingly effective solutions for protecting financial systems.

Industry case studies: Vectra and Kasisto

Fintech companies like Vectra use AI-powered technologies such as Cognito to automate threat detection and response. These systems analyse vast datasets to detect and pursue cyber threats swiftly, ensuring comprehensive security measures against malicious activities. 

Tools like Kasisto’s KAI enhance customer experiences by providing personalised financial advice through AI-driven chatbots. This demonstrates AI’s versatile applications in improving both security and service delivery within the fintech sector.

AI’s use cases in cybersecurity are expected to increase. AI will revolutionise how users are authenticated. It will use advanced biometric analysis and behaviour tracking to make it harder for unauthorised users to gain access while ensuring a smooth experience for legitimate users.

This approach strengthens security by verifying identities with methods like fingerprints or facial recognition and detects unusual behaviours for added protection. AI’s ability to learn continuously from new data means cybersecurity systems will become smarter and more effective over time, adapting quickly to new threats.

  • Artificial Intelligence in FinTech

Author

Dan Brightmore

Published

11 August 2024

Estimated Read time

4Mins

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

Author

Harry Menear

Published

5 August 2024

Estimated Read time

3Mins

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

Author

Dan Brightmore

Published

26 July 2024

Estimated Read time

3Mins

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

Author

Dan Brightmore

Published

22 July 2024

Estimated Read time

3Mins

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

Author

Dan Brightmore

Published

11 July 2024

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

9 July 2024

Estimated Read time

3Mins

FinTech Strategy and Interface joined Publicis Sapient at Money20/20 in Amsterdam for the launch of its third annual Global Banking Benchmark Survey and spoke with Head of Financial Services Dave Murphy about its findings

The third annual Global Banking Benchmark Study from Publicis Sapient draws on insights from 1000+ senior executives in financial services across global markets. The study focuses on the goals, obstacles, and drivers of digital transformation in banking.

Global Banking Benchmark Study

The study was launched during Money20/20 Europe in Amsterdam last month. Eoghan Sheehy, Associate MD, and Grace Ge, Senior Principal, highlighted the banking industry is focused on improving existing processes rather than introducing new ones. Data Analytics and AI are identified as key priorities for digital transformation. Additionally, there is a focus on internal use cases and efficiency.

Eoghan and Grace also discussed the challenges faced by the banking industry. These include regulation, competition from companies like Amazon, and the need to attract talent. They emphasised the importance for financial institutions of modernising core infrastructure. Also, building cloud infrastructure to support ongoing digital transformation. Moreover, the study notes the prevalence of the development of custom-made tools and internal use cases for AI implementation. Furthermore, Eoghan and Grace provided examples of repeatable use cases and discussed the success factors for Data Analytics and AI.

Four key takeaways from Publicis Sapient

Four key tracks came out of the study…

  • Modernising the core will always be important. But modernising the core for its own sake and also building the cloud infrastructure that supports it or allows for it to be modern. A decent chunk of the survey responders are still very focused on this. Executives are stating they want to make sure their people can make the best use of the beautiful core they’ve now built.
  • GenAI is an area of thoughtful experimentation for the Neobanks. We’re talking about scaled microservices here. Instances where, across Neobanks, you’ll have the same machine learning model and the same GenAI text generator facilitating retail and SMEs. That’s pretty sophisticated and something everyone has to contend with.
  • Data Analytics transformation is a key priority using GenAI to do so along with bringing new talent into the game.
  • Payments has been a big theme at Money20/20… We’re seeing lots of activity around ancillary individual product areas.

“The study focuses on how to think about solving problems end-to-end. Banks are dealing with legacy issues and taking a customer first view into solving the challenges. The practical application of AI across the banks is a significant theme as they look to automate decision-making and deliver better credit risk models. AI is finally delivering a set of use cases that truly can impact the way banks operate and build their own technology.” Dave Murphy, Head of Financial Services, EMEA & APAC

Be among the first to receive the study by signing up here


  • Artificial Intelligence in FinTech

Author

Dan Brightmore

Published

8 July 2024

Estimated Read time

3Mins

The RAI Amsterdam Convention Centre was the location for the world’s leading fintech conference.

Money20/20 Europe offered a unique blend of insightful keynotes, panel discussions, and networking opportunities. These underscored the transformative power of emerging technologies in financial services.

This year’s theme was ‘Human X Machine’. Money20/20 Europe explored the relationship between humans and intelligent machines, focusing on how the partnership between artificial and human intelligence will forge a new era in finance…

Innovations in AI and Open Banking

Artificial Intelligence was a major theme throughout Money20/20. A notable session featured Patrice Amann from Microsoft and Kevin Levitt from NVIDIA. They discussed the role of Generative AI in transforming customer experiences in banking. They highlighted the importance of integrating business-specific data to enhance the accuracy and effectiveness of AI solutions​​.

Open banking also garnered significant attention at Money20/20. Mastercard and bunq announced a partnership enabling users to consolidate multiple bank accounts through bunq’s AI-driven money assistant, Finn. This move is part of a broader trend towards greater financial integration and personalised banking experiences​​. Additionally, Token.io and Prommt unveiled a collaboration to improve open banking payments. This illustrated the increasing importance of seamless, user-friendly payment solutions in the fintech landscape​​.

Why Money20/20?

Fintech Strategy met with SC Ventures, Lloyds Banking Group, OSB Group, AirWallex, Plaid, Paymentology, Episode Six, Mettle (NatWest Group) and more to take the pulse of the latest trends across the fintech landscape…

Mettle

Michelle Prance, CEO, Mettle (NatWest Group): “It’s good for Mettle to come here because we are a fintech that was incubated inside a large bank for fintechs. Quite often their route to market, and capitalisation, is by going into a main bank being acquired. It’s a marriage between a big organisation and the small nimble fintech. People are really interested in what we’re doing because big incumbents want to be fast and nimble. They don’t always have the capital to invest in something like we’ve been able to do with Mettle. So, they’re interested to know the right route. Do they incubate in house? Or do they buy it in? And what’s the right way to do that without killing the culture? These are the types of interesting conversations we’ve been having here.”

Episode Six

Craig Ramsay, MD Business Development, Episode Six: “Networking is really important for us as a small company. There are lots of people here who can actually solve problems and it’s the collaboration I get quite excited about. What I’ve seen change in recent years is that the big banks are looking to find small organisations like us to figure out how to solve their payments problems. And that’s different to when I was working for a bank only a few years ago. You just have to be here at Money20/20… What I’m seeing, since we returned after Covid, is how many people from different parts of the world are coming here to actually talk to each other in person. If you’re not here at Money20/20, then it’s actually hard to be relevant in this industry.”

Read the full review here

  • Artificial Intelligence in FinTech
  • Digital Payments

Author

Dan Brightmore

Published

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

5 July 2024

Estimated Read time

4Mins

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

Author

Dan Brightmore

Published

4 July 2024

Estimated Read time

6Mins

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