Author

Dan Brightmore

Published

29 April 2026

Estimated Read time

6Mins

Andrew Power, Head of UK&I at Tricentis, on why the right approach to AI can deliver the foundation for more resilient, predictable systems

Artificial intelligence is reshaping software delivery in financial services. Code that once took teams weeks to develop can now be generated and deployed in a matter of hours. This isn’t just about faster delivery; it changes the fundamentals of how software is built and how it behaves in production.

Financial institutions have moved quickly to integrate AI across core systems, from customer operations to anti-money laundering (AML) and software development to capture efficiency and innovation gains. UK parliamentary evidence shows adoption is already widespread, with the majority of firms using AI, and more planning to follow.

But as adoption spreads and becomes more embedded within key systems, so does exposure. Risk is no longer confined to individual defects, but shaped by how quickly those defects can spread across interconnected environments.

AI has removed the limits on how quickly software can be created, but not on how confidently it can be trusted, and financial institutions can now generate and deploy code faster than they can safely validate it.

This creates a new paradox: AI is both accelerating the pace of software change and increasing the speed and scale at which failures can materialise.

Machine-Speed Failure

AI-driven development shortens the distance between change and consequence. Software updates can move through the pipeline from creation to production with significantly less friction. However, this also reduces the time available to identify, flag and contain any issues before they have an impact.

AI-driven software changes don’t just move fast, they scale fast. Unlike traditional failures, these are systemic risks. A single misstep in an AI-generated update can propagate unpredictably.

For financial services, this is especially significant when key systems are deeply interconnected, spanning complex layers of infrastructure, integrations, and third-party services. Even a minor defect can propagate quickly across systems, amplifying its impact.

What would once have been contained can now escalate, cascading across systems and causing wider disruption that affects customers, operations and, in some cases, market activity. In financial services, this is not just a technical issue but a business risk with direct implications for customer trust, regulatory compliance and financial stability. The challenge is no longer simply identifying defects but maintaining confidence in what is being deployed.

This risk is already being felt across the sector. Institutions are accelerating delivery to meet customer expectations and competitive pressures, but often without corresponding advances in validation. Tricentis’ research shows 68% of financial services organisations anticipate outages or serious incidents due to poor software quality.

Regulatory Pressure for AI is Increasing

The issue is also drawing attention from regulators. Earlier this year, the UK Treasury Committee warned that current approaches to AI in financial services are inadequate and could expose customers and the wider system to “serious harm”, highlighting the need for stronger guardrails, clearer accountability and more robust oversight to deploy it safely.

Traditional resilience frameworks were never designed for systems evolving in real time, and AI can no longer be treated as a marginal technology risk. It must become central to how organisations manage and assess resilience.

This marks a shift from software quality being an engineering concern to a board-level issue of operational resilience. If machine-speed change is the new operational hazard, then failure to address it becomes a strategic issue rather than a technical one. With that in mind, financial leaders must acknowledge AI’s dual role as both a driver of risk and a mechanism for preventing it.

AI as Both a Safeguard & Source of Risk

AI also offers the most effective and scalable way to manage the risks it introduces. Advanced AI-driven validation, continuous monitoring and risk-prioritised testing can identify issues earlier than any manual process, helping reduce the likelihood they reach production.

In effect, the same AI that accelerates software creation must now be applied to validation and governance – operating at the same speed and scale.

The same capabilities that facilitate rapid software production can be applied to validation and governance, continuously evaluating system behaviour, detecting anomalies and prioritising testing based on potential business impact, rather than volume. This allows organisations to move beyond rigid approaches and towards more adaptive, responsive quality models that more accurately reflect the way AI behaves.

Instead of relying on standard periodic testing cycles, systems can be validated on an ongoing basis. This enables earlier intervention before issues escalate.

AI can also help organisations better understand the complexity of their own systems. By analysing dependencies across applications and infrastructure, it becomes possible to identify which processes are most critical and where failures would have the greatest impact.

From Acceleration to Control

There is a clear mismatch in how financial organisations approach AI. While many are leveraging AI to accelerate development, far fewer are evolving their validation and governance to keep pace, and it’s in this gap that risk emerges.

This is the “confidence gap”, where organisations can create software faster than they can safely deploy it.

To address this imbalance, firms must treat software quality as a core component of their AI strategy. Development and validation must move forward together. Governance must adapt to continuous, AI-driven change. This requires a move from static testing and coverage metrics to continuous, risk-based validation, where software is assessed in real time based on potential business impact.

If AI is the engine driving software creation, validation must act as the braking system – built in, not bolted on at the end. At machine speed, gaps in control become points of failure. The aim is not to slow innovation, but to ensure it progresses in a way that is sustainable and safe. When validation keeps pace with development, firms can move quickly and competitively, whilst maintaining control over how risk is introduced and managed.

This is a change we are seeing across large enterprises adopting AI-driven quality approaches, where validation, monitoring and governance are increasingly orchestrated together rather than treated as separate processes.

Preventing the Next Outage

The financial sector has already seen how quickly failures can escalate in complex, interconnected environments. In March, an IT error at Lloyds Banking Group exposed the private financial information of nearly half a million customers, prompting the bank to issue £139,000 in compensation.

Such incidents aren’t isolated: over the last two years, more than 33 days of unplanned banking outages have been reported to Parliament, underlining the scale of the issue.

As AI increases the velocity of change, it also raises the stakes for getting it wrong. But the irony is that it also provides the tools needed to prevent these failures from happening in the first place. AI is both contributing to the risk of outages and becoming the most effective way to prevent them.

By applying AI to continuous validation, monitoring and risk detection, organisations can spot issues earlier, understand their potential impact and intervene before disruption occurs. This shifts the focus from reacting to outages to preventing them, and it’s where the paradox becomes constructive. AI doesn’t have to be a source of instability.

With the right approach, it can become the foundation for more resilient, predictable systems. Those that fail risk trading innovation for instability. In the AI era, speed without confidence is simply another form of risk.

Learn more at tricentis.com

  • Artificial Intelligence in FinTech
  • Cybersecurity
  • Cybersecurity in FinTech
  • Fintech & Insurtech

Author

Dan Brightmore

Published

28 April 2026

Estimated Read time

2Mins

Peer-reviewed Physical Review Research paper shows efficient preparation of financial distributions on quantum hardware

Haiqu, a leading developer of quantum middleware, has announced the publication of joint research with HSBC in Physical Review Research demonstrating an efficient approach to encoding real-world probability distributions into quantum circuits.

Quantum State Preparation

Quantum state preparation, the process of encoding classical data into quantum states, is widely recognised as a major bottleneck when implementing many algorithms on hardware. This challenge is particularly relevant for applications such as financial risk modelling and simulation, where complex probability distributions must be loaded onto quantum devices.

The research uses matrix product state (MPS) methods to construct shallow circuits that encode smooth functions, including probability distributions, directly into quantum states. It also introduces a sampling-based workflow that avoids storing the full discretised dataset in classical memory, enabling larger encoding circuits to be generated.

The approach was validated on finance-relevant models including heavy-tailed Lévy distributions, commonly used to capture extreme market events. 

On IBM quantum hardware, circuits up to 25 qubits produced samples that passed standard statistical tests, showing the method can accurately reproduce the probability distributions these models rely on in practice. 

Sampling-Based Workflow

Using the sampling-based workflow, the researchers also executed circuits up to 64 qubits, reproducing qualitative features of the target distributions under realistic device noise and demonstrating feasibility at larger scales. Similar behavior was observed in simulations up to 156 qubits, indicating the approach can extend to substantially larger problem sizes.

“Preparing complex probability distributions efficiently is a key step in many quantum algorithms,” said Dr. Philip Intallura, Group Head of Quantum Technologies at HSBC. “This work shows how they can be implemented with much shallower circuits, bringing practical applications such as financial risk modelling closer.” 

“One of the biggest practical barriers is getting realistic financial data onto today’s quantum hardware. This work shows a scalable path around that barrier and helps move quantum finance workflows from theory toward execution.” Mykola Maksymenko, Co-founder & CTO, Haiqu

Read the paper in Physical Review Research. 

About Haiqu   

Haiqu is an emerging leader in quantum software that supports the notion that near-term, commercially viable applications are achievable with the right software, even on current hardware. Haiqu’s hardware-agnostic software can run applications with up to 100x more operations on current devices compared to competitors. Headquartered in New York City in the United States, Haiqu’s expert team operates from US, Canada, Ukraine, UK, EU, and Singapore, contributing to the company’s mission to make quantum computing practical as soon as possible.

  • Blockchain & Crypto
  • Digital Payments
  • Fintech & Insurtech
  • Infrastructure & Cloud

Author

Mark Talbot

Published

22 April 2026

Estimated Read time

5Mins

Mark Talbot, Director, CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it

Many organisations have long treated improvement as something that arrives as a top-down effort, not something built with the people doing the work. Specialists designed new processes, discussed them in formal forums, and introduced them through large change programmes that often felt detached from daily work. For most employees, ‘transformation’ meant being asked to follow new rules, rather than designing better ways of working.

AI is starting to reverse that pattern. Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day. This shift – which can be described as the democratisation of AI – changes who participates in improving the business. However, it is worth remembering that this shift only works at scale when AI is embedded within a platform that maintains governance, visibility and control. 

Process Improvement in the Hands of Many

Until recently, fixing a broken process often meant filing tickets, waiting for a slot on an IT roadmap, or hoping that a specialist team would eventually address the issue. Creating applications, building automations or redesigning workflows were seen as highly technical tasks. For most employees, waste and inefficiency were things to work around, not things they had the tools or authority to change.

That obstacle is now deteriorating, as long as organizations don’t lose sight of the fact that governance remains essential, particularly in highly regulated environments

AI agents, generative AI and conversational interfaces allow people across the business to shape how work is structured. Within this model, someone in operations can describe an outcome in plain language and have an AI system propose and embed the steps within existing processes. Within a governed platform, non-technical users can adapt existing solutions and automate repetitive tasks without waiting months for central support. At the same time, process insights give developers visibility into what is being built, enabling them to refine, standardise and scale applications more quickly across the organisation.

Data is opening up as well. Data fabrics and related architectures connect scattered information sources into governed layers that a wider audience can access safely. Instead of waiting on static reports, people can access relevant, trusted data when they need it, and use AI to interpret and apply it to their decisions.

When process insight and data access reach this level, best practices move beyond documentation or occasional training. Tools and workflows embed them into daily work, improving performance across the organisation.

Scaling Improvement Across the Organisation

As more individuals understand how their work connects to broader outcomes, organisations unlock a powerful driver of change. Process improvement no longer depends only on a small group of specialists. Employees can recognise when processes are inefficient or risky and have the means to address them at scale, inside an AI platform.

By encoding domain knowledge into AI assistants and digital coworkers within an enterprise-grade AI platform, organisations can share expertise across roles and levels. These AI-powered helpers do not replace professional judgment. They strengthen it. They surface options, highlight inconsistencies and provide context, while humans make the final decision. Over time, each interaction becomes both a learning moment and a new piece of institutional knowledge that organisations can capture and reuse.

In this model, process improvement is no longer episodic or confined to formal transformation projects. It becomes part of everyday work, inside a platform with AI tools that provide real-time feedback and recommendations.

AI, Noise Reduction, and Better Oversight

This shift raises a key question: if AI platforms make analysis, decision support, and process design more accessible, what happens to deep expertise?

There is a concern that easy access to AI advice might weaken people’s understanding. If answers are always a prompt away, will teams still develop the knowledge that comes from working through complexity? If people follow AI suggestions without grasping the logic, how meaningful can human oversight really be?

Over-reliance on instant guidance can create only surface-level competence. People may treat AI outputs as instructions rather than as inputs to their own reasoning.

On the other hand, used well, AI can create more room for expertise, not less.

By handling repetitive tasks and routine decisions, AI reduces the volume of low-value work that consumes people’s time. Teams can then focus on exceptions and refine how they make decisions. Instead of dealing with every routine request themselves, they can focus on work where context and experience matter most.

When AI removes more of the routine burden, teams have more capacity to focus on judgement, process design and oversight. That helps build expertise while keeping improvement connected to the wider goals and governance of the business.

Shaping AI, Not Just Living With It

As organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. AI is moving from something that happens to the workforce to AI being something that is built and refined with the workforce.

Organisations should treat people as partners in shaping AI, rather than as operators of automated systems. When AI platforms can be combined with process visibility and human judgement, employees can have an outsized effect on the systems around them. They can influence how work is structured and how decisions are made. In that sense, AI redistributes who participates in designing better ways of working, and creates an opportunity to anchor that shift in thoughtful design and human expertise.

Learn more at appian.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech
  • People & Culture

Author

Vincent Guillevic

Published

16 April 2026

Estimated Read time

5Mins

Vincent Guillevic, Director of Fraud Labs at Entrust, argues companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers

Identity verification and tackling fraud began as a face-to-face process, built on human trust. Opening a bank account involved meeting a banker in person and from there, trust was established because both parties could see and interact with each other directly in branch.

Fast forward to the digital age and a lot of services have moved online. Identity verification has therefore shifted from in-person checks to remote identity verification. Today, we’re in an era where identity is now central to every interaction we have online.

Fraud has followed the same trajectory. Much like a burglar would test every possible entry point rather than just the front door, fraudsters probe every stage of the customer journey. They look for weaknesses at onboarding, during login, and throughout ongoing transactions and data requests.

That challenge has intensified in recent years. AI has given fraudsters faster, sophisticated and scalable tools. Deepfakes can bypass checks, AI‑generated documents can appear real, and phishing and impersonation attacks can now be automated at scale.

Once a fraudster gains access to a legitimate account, the damage escalates quickly. Global losses from account takeover (ATO) fraud were projected to reach $17 billion in 2025, up from $13 billion in 2024. While the underlying intent of fraudsters seeking the weakest point of entry, the breadth, speed and sophistication of modern attacks have.

Identity Fraud Patterns Across the Customer Lifecycle

Fraud can occur at any stage of the customer journey. From verifying identity at onboarding to securing connections and fighting fraud in everyday transactions. Each stage introduces its own risks, and attackers adapt their tactics based on where value can be extracted most efficiently.

In 2025, patterns showed a clear distinction between industries targeted for new account fraud and those targeted for account takeover fraud. Businesses that offer immediate incentives such as promotional offers or sign-up bonuses are primarily targeted for new account fraud. In contrast, businesses where accounts accumulate long-term financial or data value face higher levels of ATO.

Industries built around sign-up incentives or instance access experience most fraud at onboarding. For instance, in crypto, 67% of fraud attempts occur during account creation, largely driven by sign-up incentives. Vehicle rental follows a similar pattern, with 67% of fraud taking place at onboarding as attackers use fake identities to gain short-term access to high-value assets. In these sectors, low-friction onboarding creates opportunities to harvest incentives or establish accounts that later become avenues for future money laundering.

Account takeover fraud reflects a different strategy. Rather than creating fake accounts, attackers focus on compromising established accounts using tactics such as stolen credentials, phishing, malware, or social engineering. Entrust data shows this is most common in industries where accounts hold enduring value. In payments, 82% of fraud attempts occur after onboarding, while in professional services the figure is 62%. High-value, long-standing accounts are attractive because they enable fund transfers, loans, and access to identity-rich data, making them more valuable than newly created accounts.

These patterns highlight two critical realities. First, organisations can no longer optimise for one type of risk at the expense of another. Defending a single point in the journey inevitably leaves gaps elsewhere. Second, fraud has become highly professionalised. Modern fraud operations are organised, strategic, and adaptive, moving toward the highest rewards and the weakest controls.

Prevention Must Span the Entire Journey

If fraud can occur at any stage, prevention must operate at every stage. Organisations that implement robust, lifecycle-wide identity strategies save an average of $8 million per year in fraud-related costs. These savings come from detecting threats earlier, more accurately, and beyond a single checkpoint.

There are three areas where that lifecycle approach needs to be strongest.

Get onboarding right

Onboarding is the first opportunity to establish genuine trust. Strong Know Your Customer (KYC) or Know Your Employee (KYE) processes combine document verification with biometric checks such as face recognition or fingerprint scanning to confirm that the person applying is who they claim to be. Liveness detection adds a further layer by distinguishing real users from synthetic identities and deepfakes, which are linked to approximately one in five biometric fraud attempts.

With strong identity verification at onboarding not only reduces immediate fraud, but also limits the downstream damage caused with fraudulent accounts.

Secure existing accounts with continuous authentication

Verifying identity once is no longer sufficient. Continuous authentication, combining multi-factor authentication with biometric re-verification like facial recognition, allows businesses to protect established accounts without creating unnecessary friction for legitimate users.

Crucially, it enables authentication requirements to adapt dynamically as risk levels change, rather than applying the same static check regardless of context. In payments businesses, where most fraud targets the authentication process itself, this adaptability is key to mitigating attacks before losses occur.

Monitor behaviour in real time, not just identity

Device intelligence and behavioural signals make it possible to assess risk based on how users interact with services, flagging unusual login patterns, device anomalies, or out-of-character transactions.

As AI-driven fraud becomes more sophisticated and convincing, behavioural indicators provide another layer of ongoing fraud detection. Focusing monitoring on high-risk actions, rather than only high-risk identities closes a critical gap in traditional defences.

The Window of Opportunity

Fraud has always followed the customer journey. What has changed is the availability of advanced technology capable of tracking, analysing, and responding to threats at every stage. The key question for organisations is whether these capabilities are deployed as a connected strategy or left as isolated controls with gaps in between.

Companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers, and preserve the trust that underpins long-term digital relationships.

Learn more at entrust.com and meet the team at IFGS in London on April 21

  • Artificial Intelligence in FinTech
  • Cybersecurity
  • Cybersecurity in FinTech
  • Fintech & Insurtech

Author

Dan Brightmore

Published

15 April 2026

Estimated Read time

4Mins

Read in brochure format

FinTech Strategy is back with more key insights from the industry experts and thought leaders shaping the future of financial…

FinTech Strategy is back with more key insights from the industry experts and thought leaders shaping the future of financial services.

Read the latest issue here

Vibrant Capital: Scaling AI on Main Street

Our cover star Shadman Zafar, Founder & CEO of Vibrant Capital, is building a CIO-led model for enterprise transformation. Vibrant Capital is an operator-led investment and company-building platform focused on scaling AI in the real economy. “We don’t spray investments across hundreds of AI startups. We curate a portfolio with purpose – selecting companies that solve the real mission-critical problems CIOs face in scaling AI adoption.”

FNB: Redefining Data Science in Commercial Banking

We also hear from Yudhvir Seetharam, Chief Analytics Officer at South Africa’s First National Bank (FNB) on a data science journey characterised by curiosity, culture and the drive for a competitive edge. “Ours is a holistic approach focusing on the customer,” he explains. “Understanding the context of each customer journey and then using that context so that when we interact with you, we’re able to drive the right conversation with the right customer, at the right time, through the right channel and for the right reason. These ‘five rights’ make our interactions with clients more impactful.”

Virginia Farm Bureau: An Enterprise CIO’s Journey

Shifting focus to the world of insurance at the Virginia Farm Bureau, we spoke withan Enterprise CIO at a complex mission-driven organisation. As he approaches retirement, Patrick (Pat) Caine reflects on his career as a CIO and the centennial of an organisation renowned for resiliency, collaboration, commitment to a greater cause, diversity and service to its members. “In my role as CIO, I’ve always been that person who connects the dots between business needs and technology execution. Virginia Farm Bureau is digitally relevant, collaborative, and well‑positioned for the future.”

Mastercard: Protecting Trust in the Digital Economy

Michele Centemero, EVP Services at Mastercard Europe explains why promoting awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems, will be critical in reducing the impact of scams and protecting trust in the digital economy. “The combination of AI, robust identity controls and open banking can help protect consumers from scams, whether across card and account‑to‑account payments or in fraudulent account openings.”

Thales on AI Security: How FinServ’s Budget Priorities Signal a Boardroom Shift

Todd Moore, Global VP – Data Security Products at Thales, reveals why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation. “Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring.”

Paymentology: The First Live AI-Agent Payment Is a Test for Credit Infrastructure

Thomas Benjaminsen Normann, Product Director at Paymentology, dissects the future for agentic payments and the progress still to be made. “Agentic payments demand something more granular: a clearer account of who or what acted, under what limits, and with what right to create a liability on the customer’s behalf.”

Also in this issue, we hear from Publicis Sapient, on why asset managers must redesign their enterprise for AI-driven decision intelligence; learn from Bitpace why the most resilient payments infrastructure will be the one with the most adaptability; rank the AI maturity of 12 of the largest payments networks in the latest Evident AI Index; and round up the key FinTech events and conferences across the globe.

Enjoy the issue!

Read the latest issue here

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Cybersecurity in FinTech
  • Data & AI
  • Digital Payments
  • Embedded Finance
  • Fintech & Insurtech
  • InsurTech
  • Neobanking

Author

Todd Moore

Published

14 April 2026

Estimated Read time

4Mins

Todd Moore, Global Vice President, Data Security Products at Thales, on why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation

Financial Services organisations are responsible for some of the biggest growth in the global economy. Equally, they’re some of the most vulnerable. Like many other sectors, they’re racing to embrace AI, but with adoption comes new security risks.

According to Thales’ Data Threat Report: Financial Services Edition 81% of FinServ organisations are now investing in GenAI-specific security tools, with nearly a quarter using newly allocated budget. This surge in funding marks a turning point: AI security has moved from being an IT concern to a boardroom priority.

The fact that new budget lines are being carved out specifically for AI security signals a fundamental shift in corporate strategy. Boards increasingly recognise that protecting AI systems is as critical as safeguarding payment rails or core banking infrastructure. For an industry built on trust, resilience, and regulatory compliance, this investment wave shows how central AI has become to both risk management and competitive growth.

Balancing AI Innovation and Security

While FinServ organisations are aware of the security risks AI poses, they’re also seizing upon the opportunities it presents. The report has found that in 2024, FinServ businesses outpaced the broader market in AI deployment, leading in enabling employees to use AI and ahead in AI integration, which has continued into 2025. Additionally, 45% say they’re in the ‘integration’ or ‘transformation’ phases of their GenAI journey, compared to just 33% across wider industries.

AI’s ability to accelerate services, automate processes, and analyse data at scale makes it an exciting prospect, especially in the financial sector. This makes securing AI systems a priority for FinServ organisations, with increased GenAI integration reflecting developing organisational maturity and progress beyond experimentation.

The Risk

Yet the scale of opportunity is matched by the scale of challenge. AI systems require vast amounts of structured and unstructured data to conduct analysis and make recommendations.

For FinServ organisations, this often includes highly sensitive customer and transactional information, proprietary algorithms, and records bound by strict regulatory oversight. The risk is not only about whether AI systems themselves are secure, but whether the data they’re working from is accurate, as well as whether their adoption inadvertently creates new routes to data exposure and exfiltration.

Businesses need a clear strategy to fully understand how AI models are operating within their IT infrastructure, the applications they’re interacting with, and the data they’re accessing and pulling from.

The Response

Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring. Newly allocated budgets for AI security, with nearly a quarter of FinServ firms making such investments, show how central AI has become to board-level strategy. These investments move firms beyond reactive fixes to proactive frameworks that evolve with the technology. AI security is no longer just an IT concern, it’s a strategic priority requiring collaboration between security, compliance, and business leaders. By factoring risk into early planning, organisations can align innovation with responsibility and build resilience for the long term.

Pioneering AI Security

Building on investment in AI-specific security is only the beginning. As scrutiny intensifies, the firms that will lead are those that treat AI security as integral to business strategy, not a bolt-on layer. Success will require visibility into how models behave, continuous validation against emerging risks, and adaptive controls that evolve with the threat landscape.

The financial services organisations that embed these safeguards into their core infrastructure will protect sensitive data as well as setting a benchmark for resilience and trust in an AI-driven economy. By making AI security a boardroom priority today, these firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation.

Thales: AI is the New Insider Threat 

Thales 2026 Data Threat Report Finds 70% of Organisations Rank AI as Top Data Security Risk

Data security has taken centre stage as the success of enterprise AI initiatives increasingly hinges on consistent, controlled access to proprietary organisational data sources. The 2026 Thales Data Threat Report examines the complex calculus that organizations must undertake to enable innovation while securing their most valuable asset – their data.

This research was based on a global survey of 3,120 respondents fielded via web survey with targeted populations for each country, aimed at professionals in security and IT management. 

Read the Report

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech

Author

Lee Fredricks

Published

10 April 2026

Estimated Read time

6Mins

Lee Fredricks, Director – Solutions Consulting, EMEA at PagerDuty, on why technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability

Technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability. In 2025, too many cloud services outages and disruptions took place across the public and private sectors, and now regulatory, technological and cultural pressures are converging to say that enough is enough.

Outages often translate into broader repercussions for the organisation, including revenue impact, customer churn, share price pressure and potentially regulatory reporting obligations. Operational metrics must now be discussed alongside financial KPIs at the board level. C-suite leaders understand accountability, especially within the very regulated financial sector.

DORA’s First Birthday

It’s now been one year since the implementation of the Digital Operational Resilience Act, or DORA, introduced by the EU to strengthen the digital resilience of financial institutions. By now, organisations have had time to consider moving from mere compliance to creating a competitive edge from their investments.

Enterprise tech leaders are in the middle of a balancing act. They’re managing ongoing modernisation and transformation initiatives while navigating multi-jurisdictional regulatory scrutiny. At the same time, they face constant pressure from the board and must meet evolving customer needs—all competing for immediate attention. The stakes have never been higher. Operations teams are no longer viewed as a back-office IT function. Their success in keeping the organisation running and driving revenue is now a board-level concern.

For organisations today, IT is business delivery.

A year of DORA has seen organisations make the shift from focusing solely on mere compliance to setting meaningful demonstrable testing, third-party risk visibility and strictly mandated incident reporting timelines. Financial firms have lessened their exposure to risky situations. Payments providers aren’t only reliant on a single cloud region or SaaS supplier, or unable to provide evidence of real time incident response efforts and auditable logs after a disruption.

One benefit of these overall systemic improvements is enhanced supply chain accountability. Financial institutions and their technology partners are both liable for potential penalties and reputational risk, which makes it highly critical that they can prove their resilience capabilities.

Nevertheless, operational resilience is a continuous discipline. A fragmented incident response can expose firms to regulatory and reputational risk again and again if not addressed systemically. As such, many organisations are looking toward AI agents as part of a move towards ‘no-touch’ operations.

From Autonomy to Self-Healing

Under set policies, autonomous agents can handle incident response and operational tasks, such as detection, triage and remediation. AI agents deployed in operations may become the backbone of L1 (first contact) and L2 (more skilled) support. Contrast this with the traditional, reactive, ticket-driven model of IT. The industry can move much faster and with a higher successful close rate. Leveraging intelligent automation reduces mean time to detection/resolution and KPIs around lower incident volumes reaching L3. Additionally, it can lead to improved service availability percentages. Well integrated agents that actually support existing operations teams also help manage the issues around talent shortages faced by many organisations.

A typical incident lifecycle with agentic processes includes several stages depending on the model, but can be summarised as: Anomaly detected, correlated with recent deployment, a remediation script triggered and a human notified if set thresholds were breached. Such no-touch operations are golden in any sector, but particularly with industries such as digital banking and retail, where peak traffic periods demand near-instant response and poor customer experience is a powerful motivator for users to instantly change providers.

IT Standardisation

In addition, consider standardisation as part of strategic infrastructure best practices. There is a role for central operations clouds and operational ‘golden paths’ as solid foundations for reliable operational scale and dependability. Standardisation enables consistent, scalable operational excellence especially across large, distributed enterprises. ‘There is one way and it is the right way’ can be a great time and stress saver for operational teams – particularly if a regulatory notification and clear evidence is required.

For example, a global bank might define a single golden path for deploying customer-facing applications with pre-approved monitoring, incident response workflows, and regulatory reporting templates built in. In an outage, teams follow the same process and automatically capture the evidence required for regulators, avoiding confusion, delays, and compliance risk.

All of these possibilities take us to an exciting new place for an evolved set of developer and operational roles. When organisations enable AI to reshape daily engineering work away from manual firefighting and low-value work it frees headspace and time for developers and engineers to move into more architectural thinking and intelligent oversight of automated systems. These augmented teams will be empowered to manage simple situations instantly and devote more time and attention to the more difficult issues – the edge cases and the strategic necessities.

Enabling Agentic AI

Using another lens, businesses with agentic IT operations capabilities support their current talent, extending their reach and the speed of their response. The winning organisations will be those who deploy agents strategically, freeing up humans for that higher-value work – i.e. L3 expert support – and setting new standards for operational excellence that customers can rely on. Ideally this means making commensurate investment in existing people, training and organisational change management. A culture of continual upskilling and forecasting that points humans to where they make the best impact will be just as important as the autonomous tech tools working alongside them.

Autonomous agents allow many new services, and one of those can be described as self-healing operations. This evolution of the operations world is where predictive detection, automated remediation and embedded resilience all coalesce. With an autonomous process of testing, maintenance and remediation, organisations can focus on finely measuring improved customer trust. They can also enjoy the productivity and revenue benefits of high business continuity and availability.

AI is still a new technology, and many are legitimately concerned with the concept of autonomous agents. There is a need for clear guardrails, audit trails and explainability in automated remediation, and many technology partners have invested in their ability to support across these areas. Moreover, firms must maintain direction with policy-driven automation rather than uncontrolled autonomy, particularly in regulated industries.

Mandate Operational Excellence

This year is very likely to reward organisations that treat operational resilience as core to their business strategy. Those investing in automation, standardisation and governance will set the pace for their industries in an AI-enabled and increasingly autonomous world.

Regulators are already expanding their scrutiny and reliability expectations beyond financial services firms. Across the world, jurisdictions are increasingly looking to strengthen their economies and digital services in particular through resilience and cybersecurity measures. At the same time, agentic operations, and the organisational performance benefits they support, will rapidly become table stakes technology in all sectors. Inevitably, customers will judge brands on digital reliability as much as price or product features when evidence of outages are a click or a headline search away.

Start now. Audit internal incident response maturity, review the potentially complex web of third-party IT dependencies and identify where automation makes clear business sense. While resilience is an investment in compliance, it is also critical to ensure customer trust and future stability.

Learn more at pagerduty.com

  • Artificial Intelligence in FinTech
  • Cybersecurity in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech
  • Infrastructure & Cloud

Author

Jamil Jiva

Published

8 April 2026

Estimated Read time

5Mins

Jamil Jiva, Global Head of Asset Management at Linedata, on why the next chapter of AI-driven finance will be shaped not just by technology, but by creativity

Beyond Data: Where AI Finds Unexpected Inspiration

The discussion about training AI largely focuses on concerns that accessible, human-generated data is limited and may soon run out completely. If this is the case, how can technology that depends on a seemingly endless stream of inputs to iterate, test, and adapt deliver the results we expect? AI relies on structured, high-quality data to thrive, but what happens when we run out of spreadsheets and financial models to train AI? We need new data sources to ensure it continues to learn, adapt, and deliver accurate insights. Video games stand out as offering some of the richest, most expansive, and complex environments for AI training.

At first glance, video games and financial operations seem to belong to entirely separate worlds. However, AI connects these domains, with models leveraging virtual-world training to tackle real-world financial tasks. Financial documents such as credit agreements and tax returns are often convoluted, unstructured, and labour-intensive to process. Therefore, AI designed to interpret such data must possess strategic reasoning, real-time adaptability, and advanced pattern recognition. So, could video games be the ideal training ground?

Contrary to popular belief, gameplay can significantly improve how people think, learn, and solve problems. The abilities required to excel at video games closely reflect the skills AI systems must acquire today.

Levelling Up: What Virtual Worlds Teach Machines

Practice leads to proficiency, a principle that applies to both humans and AI. Interestingly, many of the most significant advances in AI development have emerged not from conventional data training, but from taking creative approaches. Games push AI to emulate human thinking and sharpen its statistical intuition.

These game-trained models are neither expensive nor heavily reliant on resources, and they sidestep the issue of data scarcity. As a result, they are actively shaping the future of financial intelligence. The examples below offer a clear demonstration of the potential of gameplay.

Virtual Economies: Lessons from World of Warcraft

World of Warcraft, with millions of players interacting in an immersive and dynamic world, features an economy that closely mirrors real-world financial systems, complete with inflation, supply and demand cycles, and fraud risks. The game even inspired one of the most renowned epidemiological studies: when the in-game ‘Corrupted Blood’ plague spread unpredictably, scientists used it as a model for real-world pandemic simulations.

Financial models depend on vast, interconnected data networks, much like the economy in World of Warcraft. Organisations employ AI to continuously monitor patterns, detect anomalies such as fraud or misstatements, and optimise data extraction for financial reporting, mirroring the way AI analyses virtual economies.

Urban Chaos: GTA V and Real-World Simulation

While Grand Theft Auto (GTA) V is famous for its open-world chaos, researchers have leveraged its traffic systems and non-player character behaviours to train AI for applications such as self-driving cars, crime pattern recognition, and urban planning. At its heart, GTA provides a platform for AI to process vast amounts of unstructured data in real time.

Similarly, financial institutions manage millions of data points from a wide range of sources. Their AI tools must automatically extract insights, classify information, and normalise complex formats. GTA serves as a controlled yet intricate environment for simulating scenarios, enabling AI to optimise for real-world tasks through ongoing feedback loops.

Sandbox Creativity: Minecraft and Adaptive Thinking

Minecraft provides a sandbox environment where AI learns through exploration. OpenAI even trained an AI to play Minecraft by watching YouTube tutorials, closely mimicking the way humans learn. Similarly, any AI used by financial institutions must be able to self-learn from new document types and structures, adapting just as a Minecraft AI learns to survive.

Reinforcement learning, where AI improves based on feedback, is a key element of intelligent document processing. Thanks to its vast scalability and dynamic, hierarchical environments, Minecraft serves as an ideal setting for navigation and repeated feedback loops, helping models develop domain-flexible reasoning.

Multiplayer Mayhem: Dota 2 and the Art of Teamwork

Dota 2 stands out as one of the most complex competitive games ever created, presenting AI with challenges in real-time decision-making, strategic coordination, and adaptability. OpenAI Five, trained on the equivalent of 45,000 years of gameplay within just 10 months, managed to defeat renowned, professional human teams. As anyone who has mastered StarCraft knows, tactical adaptability is essential for gaining the upper hand.

Financial institutions operate in environments that are just as dynamic as the shifting levels of a video game. Market conditions, regulations, and data formats are in constant flux. AI must be able to adjust to new document structures, handle missing information, and navigate edge cases, much like AlphaStar adapts to an opponent’s unpredictable strategies.

From Pixels to Profits: Bringing Game Logic to Finance

Whether to streamline operations, mitigate risks, or make informed decisions in today’s data-intensive financial landscape, AI has the potential to fundamentally transform financial offerings, delivering personalised and evolving experiences that foster understanding and combine seamlessness with regulatory compliance.

Yet AI does not simply require more data from which to learn; it needs better data. Video games offer near limitless, pre-built, highly complex digital worlds where AI can test hypotheses, simulate scenarios, and refine decision-making models. By utilising these unique environments, AI is challenged to enhance its speed, accuracy, and efficiency. 

The world of video games has many lessons we can learn when building AI, and given AI’s remarkable ability for transferable learning, it makes sense to leverage these pre-trained models to power essential financial workflows. It is more than just document processing; it is thinking, and the same intelligence that enables AI to defeat world champions in Dota 2 is now driving the next generation of financial AI solutions.

The next chapter of AI-driven finance will be shaped not just by technology, but by creativity. By embracing unconventional data sources such as the immersive complexity of video games, industry leaders will unlock new possibilities for personalisation, security, and customer engagement.

Learn more at linedata.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech
  • Neobanking

Author

Richard Doherty

Published

Estimated Read time

6Mins

Richard Doherty, Head of Wealth & Asset Management, Publicis Sapient, on how asset managers must redesign their enterprise for AI-driven decision intelligence

The asset management industry is entering a structural inflexion point. The first wave of AI focused on improving productivity through copilots and automation. The next wave will fundamentally reshape how decisions are made, executed, and governed across the enterprise. This is not a technology upgrade. It is an operating model shift.

Despite significant investment, many firms remain trapped in fragmented AI experimentation. A majority are yet to realise meaningful economic returns from AI, not due to lack of capability, but due to a failure to redesign how intelligence is applied across the organisation. The gap between ambition and outcome is not a technology problem. It is a structural one.

From Automation to Decision Intelligence

The industry conversation has evolved. The question is no longer whether to adopt AI, but how to scale it across the enterprise. However, most firms are still approaching this challenge through the lens of automation, identifying tasks that can be executed faster or at lower cost. This delivers incremental value, but does not address the underlying constraint: the structure of decision-making within the organisation.

Traditional operating models are built around sequential workflows. Work moves from function to function: research, compliance, operations, and distribution, each dependent on the previous stage. This creates latency, duplication, and fragmentation. Agentic operating models shift the focus from tasks to decisions.

Instead of asking “Which processes can we automate?”, leading firms are asking: “Which decisions can be augmented or owned by intelligent systems?”

This shift enables organisations to move from sequential workflows to parallel decision systems; from human-led analysis to AI-assisted reasoning; from periodic insight to continuous intelligence. The result is not a marginal improvement. It is a step-change in how the enterprise operates.

The Pressures Driving Change

This transformation is not happening in a vacuum. Asset managers face mounting structural pressures: margin compression driven by fee pressure and passive competition; rising operational complexity from regulation and product proliferation; and advisor capacity constraints that limit scalable growth. Agentic operating models directly address all three.

By automating complex workflows, rather than individual tasks, firms can significantly increase advisor and analyst capacity without proportional cost increases. Parallel decision systems reduce the time required to launch products, respond to market events, and deliver client insights. This compresses cycles from months to days. Continuous monitoring of guidelines, portfolios, and operational processes reduces exposure to regulatory breaches and operational failures.

These are not theoretical benefits. They represent measurable improvements in cost-to-serve, time-to-market, and operational resilience.

Not all Intelligence is the Same

To scale AI effectively, organisations must recognise that not all problems require the same type of intelligence. Enterprise AI operates across three distinct layers, and conflating them is one of the primary reasons AI initiatives fail to scale.

Deterministic systems execute predefined rules with complete consistency. They are essential for functions where there is zero tolerance for error, trade validation, settlement processing, and regulatory reporting. If a business outcome must be identical every time, deterministic logic remains the correct approach.

Predictive systems use historical data to forecast outcomes. Applied in areas such as portfolio risk modelling, fraud detection, and client churn prediction, they generate probabilities and insights, but they do not interpret context or make decisions independently.

Agentic systems operate where problems require interpretation, judgment, and contextual understanding, investment guideline interpretation, regulatory document analysis, portfolio insights, and client communication. These systems can reason across complex information, generate insights, and take action within defined boundaries.

The ‘Different but Valid’ Dilemma

A critical challenge in adopting agentic systems is understanding how they behave. Traditional software produces identical outputs. Agentic systems produce reasoned outputs.

This introduces what I call the ‘different but valid’ dilemma. An agent may take a different reasoning path from a human and arrive at a different, but still correct, conclusion. This variability is not an error. It is inherent to reasoning systems.

The real risk lies in hallucination, outputs that are not grounded in data or evidence. Managing this requires organisations to clearly define where variability is acceptable. All AI-driven processes sit on a spectrum: deterministic actions with no variability (trade execution), predictive actions with controlled variability (risk scoring), and agentic actions with higher variability (investment insights).

Leading firms design systems where agents perform reasoning, deterministic systems enforce execution, and humans retain oversight on high-consequence decisions. This balance enables both flexibility and control.

The Operating Model Shift

The most significant change is not technological; it is organisational. Traditional models are built on functional workflows. Agentic models are built on coordinated decision systems.

Consider what launching a new investment product looks like under each model. In a traditional model, it involves sequential handoffs between teams, compliance reviews the guidelines, operations configures the systems, and distribution drafts the client narrative. Each stage waits for the last.

In an agentic model, intelligent systems operate in parallel: compliance agents interpret guidelines, operations agents configure constraints, distribution agents generate client narratives, and governance agents validate outputs. This orchestration compresses timelines, reduces friction, and enables continuous decision-making. It represents a fundamental redesign of how work is performed.

Governance: the Foundation for Trust

Trust is the prerequisite for scaling AI. Without it, adoption stalls, not because the technology fails, but because the organisation cannot adequately explain or defend the decisions it makes.

Leading firms implement governance models built on three principles. First, explainability: every decision must be traceable and auditable. Second, authority boundaries: agents operate within clearly defined limits. Third, human oversight: high-consequence decisions remain under human control.

Regulatory expectations will continue to evolve, but one principle remains constant: organisations must be able to explain how decisions are made.

Scaling AI is a Leadership Challenge

Executives must take a deliberate approach across four areas:

  • Define the intelligence model: map business problems to deterministic, predictive, or agentic systems.
  • Build the foundation: invest in data, infrastructure, and orchestration capabilities.
  • Redesign the operating model: shift from workflows to decision systems.
  • Implement governance to ensure transparency, control, and compliance.

Start with high-value use cases and expand rapidly across the enterprise. The firms that act now will establish a structural advantage in cost, speed, and decision quality. Those that do not risk being constrained by legacy operating models that cannot scale with the demands of modern markets.

The Question is not if, it is Who

The industry is not simply adopting new technology. It is redefining how decisions are made. The firms that succeed will not be those that deploy AI tools in isolation. They will be those who design the right form of intelligence for each problem, redesign their operating models around intelligent systems, and scale agentic capabilities across the enterprise.

This shift is already underway. The question is no longer whether it will happen. The question is which firms will lead, and which will be forced to follow.

Learn more at publicissapient.com

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech

Author

Dan Brightmore

Published

2 March 2026

Estimated Read time

2Mins

Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams…

Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams to identify and respond quickly, whilst also meeting regulatory timeframes for handling complaints and supporting vulnerable customers.

Netcall: AI-Powered Sentiment

The specialist bank has worked with Netcall to deploy AI-powered sentiment analysis using Netcall’s Liberty Create platform. The solution reduces manual effort and improves operational efficiency by bringing customer emails from multiple mailboxes into a single interface. Incoming messages are automatically analysed to identify dissatisfaction, highlighting cases that may require faster intervention. This allows urgent cases to be prioritised, helping HTB to resolve issues before they escalate and improve the customer experience.

“Our AI-powered sentiment analysis solution rapidly processes vast amounts of email data. Its efficiency allows our team to focus on resolving customer enquiries and issues rather than sorting priorities. The streamlined process ensures swifter responses and better customer outcomes, upholding our reputation for exceptional customer service.” Ed Eames, Head of Customer Savings Operations at Hampshire Trust Bank.

The application was built by the Hampshire Trust Bank development team using Liberty Create. It worked closely with Netcall to integrate AI sentiment analysis into existing processes. Customer-facing teams were involved throughout to ensure the solution aligned with established workflows and regulatory requirements.

Customer Service Control

A key benefit of the approach is the level of control it gives internal teams. Keywords, sentiment thresholds, and classifications can be adjusted directly. This allows rapid refinement as customer behaviour changes or new regulatory considerations emerge, without waiting for development cycles.

“Liberty Create has enabled my development team to work with remarkable agility. The ability to rapidly create and refine applications to meet ever-evolving business needs has significantly enhanced our efficiency. This allows us to deliver a wealth of new features to end users and customers with speed. With the integration of AI, we’ve been able to advance our processes while ensuring exceptional customer service. Our Sentiment Analysis application launch is a prime example of this.” Trina Burnett, Head of Engineering at Hampshire Trust Bank.

The sentiment analysis system also supports automated and ad-hoc reporting. This provides a single source of insight into customer interactions and actions taken. This helps reduce manual effort, supports audit and compliance activity, and enables teams to continuously improve customer service operations.

“As scrutiny around customer experience and accountability increases across UK financial services, the ability to listen, adapt and respond at pace is becoming a defining capability for banks seeking to maintain trust and service standards,” said Alex Ballingall, Key Account Manager at Netcall.

“HTB’s approach shows how banks can use AI-driven insight practically. Turning customer communications into faster action without adding operational complexity,” Ballingall concluded.

About Netcall

Netcall is a leading provider of low-code and customer engagement solutions. A UK company quoted on the AIM market of the London Stock Exchange. By enabling customer-facing and IT talent to collaborate, Netcall takes the pain out of big change projects. It helps businesses dramatically improve the customer experience, while lowering costs. Over 600 organisations in financial services, insurance, local government and healthcare use the Netcall Liberty platform to make life easier for the people they serve. Netcall aims to help organisations radically improve customer experience through collaborative CX.

Learn more at netcall.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Payments
  • Digital Strategy
  • Fintech & Insurtech
  • InsurTech

Author

Patrick Cooney

Published

27 February 2026

Estimated Read time

6Mins

Patrick Cooney, CFO at Version 1, on why, in an AI-driven operating environment, financial discipline is more important than ever

Over the last decade, digital transformation has become part of the CFO’s remit. As organisations invested in automation, cloud and data platforms, finance leaders were well placed to oversee spend, drive efficiency and ensure technology investments delivered measurable returns. As artificial intelligence (AI) moves from experimentation into the core of how organisations operate, that model is beginning to evolve. Primarily because AI-scale transformation demands a different balance of expertise.

A recent move by Coca-Cola illustrates this shift. The decision to take digital strategy out of CFO John Murphy’s remit and appoint Sedef Salingan Sahin as the company’s first Chief Digital Officer is not a rejection of finance-led transformation. It reflects a practical reality. While strong financial discipline remains essential, the architectural complexity and technical depth required to embed AI across an enterprise now go beyond traditional finance capabilities alone.

This raises a critical question for financial services leaders. If AI is now a balance sheet issue — shaping cost structures, risk exposure and long-term value — what should the CFO’s role look like in the years ahead?

AI is Changing How Finance Operates

In all industries, AI is no longer confined to innovation labs or isolated pilots. It is increasingly embedded in how organisations operate, make decisions and manage risk. At Version 1, our earliest focus on AI was external: helping partners use AI to transform their own businesses. We have quickly turned the lens inward. Over the past quarter, we have accelerated the use of AI across our own finance and operational functions, implementing a wide range of practical use cases that fundamentally change how work gets done.

Some of these are relatively simple but have had a significant impact. Using AI to summarise documents, generate meeting notes or surface insights from large volumes of information has become normal and is already saving time across the organisation. Others are more structural. In finance, we are applying AI to areas such as accounts payable, accounts receivable and general ledger reconciliations, where large datasets and repetitive processes create natural opportunities for automation and acceleration.

We are also rethinking reporting itself. Rather than manually producing variance analyses each month, we are developing standardised prompts that allow AI to highlight key trends, explain deviations from budget and surface insights that would traditionally take hours to compile. These are not abstract efficiencies. Rather, they directly affect the speed, quality and value of financial decision-making.

What is striking though is the pace of change. Even over the past few months, usage has increased exponentially as people find new ways to integrate AI into their daily work. This is no longer an optional experiment. AI is reshaping how organisations function from the inside out.

Modern CFOs Deliver Stewardship and Governance

One of the biggest challenges CFOs face with AI is that traditional ROI models struggle to capture its true impact. Unlike earlier waves of digital transformation, AI does not deliver value solely through cost reduction or headcount optimisation. Increasingly, its value lies in better planning, faster decision-making, improved risk management and higher-quality outputs.

I see this clearly in how we use AI for planning. Recently, we fed a combination of internal data, previous plans and external consultancy material into a large language model and spent time crafting a detailed prompt. The output was a first-pass design for a major simplification programme (including workstreams, resourcing requirements and sequencing) that would previously have taken weeks to develop.

It is worth noting that this new process didn’t replace human judgement – it dramatically accelerated it. We are using similar approaches to shape annual finance priorities, drawing on historic plans and organisational context to generate structured, actionable starting points. This kind of value is real, but it does not always show up neatly in short-term financial metrics.

At the same time, the risks associated with AI are increasing. Model drift, regulatory scrutiny, data security and vendor dependency all carry financial implications. This is why governance matters as much as innovation. At Version 1, we have put formal structures in place, including an AI oversight committee that reviews and approves new tools, ensures appropriate controls are in place and sets clear boundaries around responsible use. We tightly manage which platforms can be used and how data is protected, recognising that public, uncontrolled tools pose unacceptable risks in an enterprise environment.

This combination of accelerating value and growing risk is precisely why ownership models are changing. Many CFOs continue to play a leading role in digital transformation, with research showing that around three-quarters of finance leaders now prioritise digital strategies at the highest levels of the organisation.

People Remain at the Heart of AI Adoption

As AI scales, the CFO’s role is shifting from delivery ownership to strategic stewardship. Finance leaders are uniquely positioned to connect technology ambition with financial reality, ensuring AI investments are governed properly, aligned to business outcomes and measured over time.

This aligns closely with how we think about our own operating model at Version 1. We use what we call a ‘strength in balance’ business model, built around three equally important pillars: customers, people and a strong organisation. That final pillar includes financial performance, risk management, cybersecurity and governance, all areas that become more critical, not less, as AI adoption accelerates.

People are central to this conversation. AI inevitably raises questions about job impact and cost optimisation, and organisations have a responsibility to approach this responsibly. That means clear communication, strong change management and treating people fairly where roles evolve. It also means investing in training and enablement. We have rolled out organisation-wide AI training focused on responsible use, and we are developing a network of AI champions with deeper skills who can identify and build use cases without relying solely on central teams.

The most effective model I see emerging is a shared one. Specialist digital leaders focus on building and embedding AI capabilities at scale. CFOs retain accountability for financial discipline, data governance and value realisation. When these roles work in partnership, organisations are far more likely to capture the value they expect from AI.

Financially Guided Value Delivery

As AI becomes a baseline capability rather than a differentiator, debates about who ‘owns’ digital strategy are becoming less relevant. The more important question is how organisations ensure AI investments deliver measurable, sustainable value. For CFOs, AI is now undeniably a balance sheet issue.

Investment in the latest technology affects cost structures, risk exposure, governance and long-term resilience. Those who engage proactively, shape governance and demand disciplined value creation will help their organisations unlock lasting advantage. Those who remain passive risk inheriting complexity, cost and compliance challenges that are far harder to unwind later.

In an AI-driven operating environment, financial discipline is not diminished. It is more important than ever.

Learn more at version1.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech

Author

Dr. George Papamargaritis & Dr. Konstantia Barmpatsalou

Published

26 February 2026

Estimated Read time

4Mins

Obrela’s Dr. George Papamargaritis (EVP MSS) and Dr. Konstantia Barmpatsalou,  (Blue Team Support Manager) on why embracing a risk-led cybersecurity model will leave financial organisations better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems

Cybersecurity in the financial sector was once viewed as a compliance-driven discipline. But as attackers have increasingly targeted institutions with sophisticated, persistent and often internally driven campaigns, it has become a strategic priority.

According to the Digital Universe Report H1 2025, financial services were the second most targeted industry globally, accounting for 19% of all observed cyberattacks. This reflects both the sector’s value to adversaries and the complexity of the digital ecosystems it now operates within.

Regulatory frameworks such as the FCA and PRA’s operational resilience rules, the EU’s Digital Operational Resilience Act (DORA) and NIS2 have strengthened baseline protections. However, the report’s findings demonstrate that regulation alone cannot deliver true cyber resilience. Institutions must adopt a strategic, risk-led approach that looks beyond compliance to understand real threats, behaviours and operational dependencies.

Tailored, Internal and Stealthier Threats

One of the most striking insights from the report is how targeted financial sector attacks have become. Industry-specific security risks now represent 32% of all incidents in the sector. This is an indication that adversaries are designing attacks using detailed knowledge of financial operations, from trading workflows to payment systems.

Internal activity is also a major concern. Suspicious internal activity accounts for 26% of detections across financial services, reflecting the frequency of compromised accounts, misused privileges and lateral movement. For a sector historically focused on defending the perimeter, this shift highlights the need for deeper visibility into user behaviour and identity-driven risks.

The wider threat landscape reveals adversaries are moving away from overt, signature-based attacks. In H1 2025, brute force activity made up 27% of global alerts, while vulnerability scanning accounted for 22% and known malicious indicators for 20%. Notably, direct malware payloads dropped to 0% of trending alerts, replaced by fileless techniques and living-off-the-land methods that bypass traditional defences.

For financial institutions, this is a challenge. Many compliance requirements still centre on endpoint protection, patching and malware controls. These will of course, remain important, but they cannot address threats that are increasingly behavioural, stealth-driven and identity-focused.

Operational Complexity

The financial sector’s cyber risk is intensified by its expanding operational footprint. Cloud adoption, open banking, digital identity models and extensive third-party ecosystems have all created new points of exposure. Financial services operate within a global digital infrastructure that is both vast and increasingly interconnected. This level of complexity cannot be effectively protected through compliance checklists alone.

Regulators are recognising these realities. DORA’s emphasis on ICT third-party risk, operational resilience testing and continuous oversight reflects the need for more proactive, intelligence-driven approaches. But DORA still only sets a minimum standard. True resilience requires institutions to move beyond regulatory expectations and embed cybersecurity into broader business strategy.

Strategic, Risk-Led Cybersecurity

A risk-led approach begins with understanding the threats that pose the greatest risk to operations and customers. Financial institutions remain priority targets for groups such as FIN7, TA505, Cobalt Group and various state-backed actors. Their tactics, such as credential harvesting, remote access tools, web-injection frameworks and lateral movement, are specifically designed to exploit the digital fabric of financial services.

This evolving threat profile puts identity and behaviour at the heart of cyber defence. With credential-driven and internal threats so prevalent, institutions must prioritise behavioural analytics, continuous authentication and zero-trust models that verify users and devices contextually rather than relying on static controls.

Strategic cyber resilience also needs to have continuous assurance. Traditional audits, annual testing and scheduled penetration exercises cannot keep pace with rapidly evolving threats. Leading institutions are shifting toward continuous control monitoring, automated attack simulation and persistent adversarial testing. These practices align with the Bank of England’s CBEST framework and demonstrate a sector-wide move toward ongoing, intelligence-led assurance.

Crucially, cyber risk must be treated as an operational issue, not just a technical one. Embedding cybersecurity into enterprise risk management, financial planning, product development and board oversight is essential. This integrated approach also mirrors the direction of FCA and PRA regulation, which increasingly emphasises governance, accountability, and resilience across the entire organisation.

Beyond Compliance

Financial services underpin national economies and public confidence. As digital ecosystems grow and adversaries become more sophisticated, the sector faces a dual challenge: meeting rising regulatory expectations while defending against complex, targeted attacks. It is clear that cybersecurity must evolve from compliance-driven activity to a strategic capability built on intelligence, continuous assurance and behavioural insight.

Institutions that embrace this risk-led model will be better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems.

Learn more at obrela.com

  • Cybersecurity
  • Cybersecurity in FinTech
  • Digital Strategy
  • Fintech & Insurtech
  • InsurTech

Author

Gregory Mostyn

Published

24 February 2026

Estimated Read time

5Mins

Gregory Mostyn, CEO and co-founder of Wexler, on why the era of generalist AI tools is over, and how the future will focus on high-precision AI designed for specific industries

For decades, the UK’s professional services sector, including areas such as Law, Insurance, and Wealth Management, has argued that its business value is locked in its access to proprietary data and the specialised labour required to navigate it. Investors, lured by the moat of institutional knowledge, priced these companies accordingly. However, the first quarter of 2026 has seen significant AI disruption within the professional services market. The catalyst wasn’t a single event, but rather a move by foundational model providers that turned the industry’s most defensible assets into commodities. 

When Anthropic launched its specialised legal AI plugin, OpenAI integrated a real-time insurance underwriting engine directly into its interface, and Alturist Corp automated bespoke tax strategies, the market reacted harshly. As professional services titans such as RELX, MoneySuperMarket, and St James’s Place saw their share prices decline by more than 10% in a matter of hours, the message became clear: the era of treating AI as a ‘future risk’ is over. 

The market has been awoken to the fact that foundational AI models are no longer just plugins or nice ‘add-on’ tools; they are competitors. The move by foundation-model providers into professional services – like the legal sector – is not a one-off shock, but rather an inevitability. 

The Proliferation of Information 

Historically, a law firm’s competitive advantage was its access to information – repositories of case law, proprietary research, and historical contracts. Investors and clients valued these companies on the assumption that this data constituted an impenetrable barrier to competitors. Before AI entered the mainstream, the cost of extracting actionable information from thousands of pages of data required a small army of junior associates and hundreds of billable hours. 

In 2026, that moat has mostly evaporated. Recent benchmarks show that frontier models now achieve 80% accuracy on complex documents, compared with the 71% average of a human associate. More importantly, they do it at a fraction of the cost. It is now estimated that the inference cost for a system at the level of GPT-3.5 dropped by more than 280-fold between November 2022 and October 2024. It’s predicted that UK law firms will reduce their chargeable hours by 16% through the implementation of AI. 

The narrative that AI would be able to handle only ‘low-level’ tasks, such as NDAs or simple contract summaries, has all but evaporated. Anthropic’s move into high-stakes litigation support validates this trend. 

AI – From Swiss Army Knives to Scalpels 

An error made by many law firms when AI became entrenched within the market was to treat it as a ‘plug-in’, a nice-to-have built onto existing internal software. Many adopted general-purpose tools, often referred to as ‘Swiss Army knife’ solutions, that covered the breadth of legal work but lacked the precision, jurisdictional nuance, and risk-weighted requirements for high-stakes professional services. 

The 2026 market reaction highlighted the needs of a ‘scalpel’ approach – those that go deep in a specialised vertical within a legal workflow. For example, instead of a junior associate spending billable hours searching through case files to establish the facts of a case, they could use a ‘fact intelligence’ platform that can automate that process into minutes, whilst increasing accuracy by 95% versus 78% for human reviewers and up to 90% savings in large-scale litigation. The market is no longer rewarding firms for having information. Rather, it rewards those who can apply it at the lowest possible cost and friction. 

Reallocating Capital Across Professional Services

We’re already seeing investors withdrawing from the traditional software market and reallocating that capital into specialised AI firms. However, the risk for legacy players is that they are being disrupted from both ends. From the bottom, they are losing the efficiency game to generalist foundation models from companies such as OpenAI and Google, which are commoditising the ‘knowledge’ aspect of professional services, including basic advice and contract drafting. At the top, they are losing the expertise game to specialised firms that use AI as a precision instrument; their overhead would be lower than that of a traditional Magic Circle firm, allowing them to undercut prices while maintaining profit margins. 

The result is a massive reallocation of capital. Investments into vertical AI (AI built for one specific industry) are expected to surge to $115 billion by 2034. The market no longer bets on labour with tools, but on autonomous workflows. Investors have realised that the value lies in the middle layer – the software that sits between a general foundation model and a specific industry’s needs. 

Innovation or Obsolescence 

So far, the first market fluctuation of 2026 has taught us that you cannot outrun new technologies. To survive, firms must stop treating AI as an add-on and treat it as a foundation for their core business infrastructure. 

For UK professional services, the choice is no longer whether to adopt AI, but whether they can evolve quickly enough to avoid becoming the training data for companies building foundational models. The firms that remain in 2030 will recognise that the competitive landscape has changed. You’re not just competing with your peers, but with the compute cycles of the world’s most powerful AI labs. 

The era of generalist AI tools is over, and the future will focus on high-precision AI designed for specific industries. 

Learn more at wexler.ai

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech

Author

Trilliam Jeong

Published

12 February 2026

Estimated Read time

4Mins

Trilliam Jeong, CEO at WealthBlock on why pairing credit discipline with real-time reporting will deliver a better position to hold onto investor confidence

There’s no shortage of noise around the direct lending market right now. On one hand, deal activity remains strong, capital continues to flow in and investor appetite hasn’t wavered. On the other, competition is fierce, rates are edging down and macro conditions are less forgiving than they were a year ago.

But strip out the headlines and the fundamentals still look solid. The demand is there, both from borrowers looking for speed and flexibility and from investors chasing yield and consistency. That puts direct lenders in a strong position, provided they’re prepared to adapt.

Operational Shift

One of the most significant shifts underway is operational. We’re seeing real adoption of technology across the mid-market from AI-assisted onboarding to fully digitised investor dashboards. This isn’t just cosmetic. Faster processes and clearer visibility mean capital can move more quickly, investors stay better informed and managers have more room to protect margins, even in a tightening spread environment.

LP expectations are shifting too. Many now expect a consumer-grade digital experience from the platforms they commit capital to. They want real-time access to reports, frictionless communication and clarity around how their money is being deployed. That shift in expectations is accelerating the tech arms race across the mid-market. It’s no longer about who can show the best deck but rather can deliver the best infrastructure. And as investor sophistication grows, that infrastructure is becoming a non-negotiable.

Digital Infrastructure

That shift is also influencing how mandates are awarded. Institutional investors increasingly view digital infrastructure not as a bonus, but as a sign of long-term readiness. Questions that once focused solely on deal pipeline and past performance now extend to data availability, reporting cadence and system resilience. It’s not just about what a manager can deliver but how transparently and reliably they can do it. As more allocators run tighter operational due diligence processes, digital maturity is quietly becoming a competitive edge. Platforms that can demonstrate consistent, tech-enabled processes are better positioned to win, and keep, capital.

That matters, because rates may not stay where they are. Increased competition is already putting pressure on pricing. But firms with strong digital infrastructure are better placed to absorb it. Operational leverage, not just headline yield, is becoming a key differentiator.

Scaling Up

There’s also the issue of scale. Consolidation is real and it’s reshaping the market. The biggest managers are only getting bigger and their resources are hard to match. But size alone isn’t the whole story. Technology is giving smaller and mid-sized players a way to compete on experience even if not on balance sheet. A seamless, professional, tech-forward investor journey can carry real weight with LPs, particularly those who value speed and clarity over brand.

That’s especially relevant for new entrants. There’s no shortage of managers in direct lending and standing out requires more than just a different strategy. Yes, some are carving out a niche in NAV lending, venture debt or structured credit but what really earns attention is trust. That comes from clear communication, repeatable processes and a level of transparency that goes beyond the marketing deck.

The Outlook for Lending

The macro outlook is part of the equation too. With corporate defaults expected to rise, discipline is going to matter more than it has in recent years. Underwriting strength, sponsor alignment and proactive portfolio monitoring are back in focus. Investors will be watching for signals that managers are prepared for downside risk. The tougher the environment, the more exposed weaker systems become. Inconsistent reporting, vague valuation logic or delayed updates might have been tolerated in a bull market – but not now. Allocators want to know how a manager will behave under stress, not just how they perform when everything’s going to plan. That makes operational maturity as important as deal-level returns.

Firms that pair credit discipline with real-time reporting will be in a better position to hold onto investor confidence. Allocators are already asking more pointed questions and looking for managers who can back up claims with data. There’s still plenty of room to grow in direct lending, but it won’t be enough to rely on past performance or broad market tailwinds. The firms that outperform from here will need to be efficient, responsive and trusted. In a more competitive, more transparent and more regulated market, those are the traits that will endure.

Learn more at wealthblock.ai

  • Blockchain & Crypto
  • Embedded Finance
  • Fintech & Insurtech

Author

Dan Brightmore

Published

21 January 2026

Estimated Read time

2Mins

New research from myPOS, the European payments provider for small and medium-sized businesses, reveals that Britain’s shift toward tap-to-pay is leaving…

New research from myPOS, the European payments provider for small and medium-sized businesses, reveals that Britain’s shift toward tap-to-pay is leaving traditional PIN codes behind. As contactless becomes the country’s top payment preference, almost a third of young adults now admit they can’t remember the four digits once central to everyday spending.  

myPOS data reveals 29% of Gen Z struggle to remember, or have completely forgotten, their PIN. Highlighting how digital-first habits are shaping consumer behaviour. However, it isn’t just younger groups that are feeling the effects. One in five Boomers (20%) say they face the same issue as reliance on physical cards significantly declines. 

Contactless Payments

This shift has been driven largely by the dominance of contactless card and mobile payments. Over two-thirds of Brits (69%) say tapping, via card, mobile phone, or smartwatch, is now their primary method of payment. In contrast, just 16% rely mainly on chip and PIN, and only 14% primarily use cash. A further 10 % of Brits now live entirely wallet-free, using only their mobile or smartwatch for day-to-day spending. 

Convenience-led behaviours are accelerating the decline of PIN usage across the UK. Nearly half of British consumers (47%) say they would happily go completely contactless if it meant shorter queues in shops and venues. Flexibility and convenience (42%) and speed (34%) remain the largest drivers behind the rise of tap-to-pay.  

“As the UK embraces contactless and mobile payments, it’s clear that the traditional PIN is becoming less central to everyday transactions. Businesses and payment providers should ensure security and convenience go hand-in-hand, while recognising that consumer habits are evolving rapidly.”

Michael Ault, Country Manager at myPOS UK

  • Digital Payments
  • Fintech & Insurtech
  • Neobanking

Author

Dan Brightmore

Published

19 January 2026

Estimated Read time

5Mins

Michael Ault, Country Manager at integrated payments specialists myPOS, offers strategic advice for SMEs looking to scale through digital transformation and diversification

Scaling a small business is one of the most rewarding, yet complex journeys for any entrepreneur. While growth brings opportunities for greater reach, higher revenue, and stronger market presence, it also demands foresight, discipline, and the ability to manage risk strategically. Securely integrating new technology is the main obstacle for 47% of SME’s, even though 76% of these businesses intend to expand their IT investment. This underscores a key point of tension, as many businesses want to grow through digital transformation but struggle to do so securely and sustainably.

The business landscape continues to evolve with changing customer expectations, technology, and economic conditions. For UK SMEs, the key to long-term success lies in achieving growth but also in building resilience. Sustainable scaling comes down to three principles: embracing technology pragmatically, diversifying intelligently, and investing in people and partnerships that strengthen resilience.

Leveraging Digital Transformation

Digital transformation is the foundation of business growth, especially for small business. Cloud-based solutions, automation, and data analytics help to streamline operations, reduce inefficiencies, and create better customer experiences. However, transformation must be purposeful, not performative.

The smartest approach is to scale technology investment incrementally, integrating flexible, modular systems that evolve with business needs. This approach not only lowers risk but also helps ensure digital maturity evolve over time. When SMEs use modular, cloud-based technology, operations run more smoothly and changes can be effectively analysed. Ultimately, resilience is not built through one-time upgrades but through a culture of continuous digital evolution.

Diversifying Revenue Streams

Depending on a single product, service, or market leaves a business vulnerable to sudden changes in demand. Diversification, when guided by customer insight and data can turn volatility into opportunity. Expanding into online sales, introducing subscription models, or targeting fresh customer segments can make income streams much more stable and sustainable.

At myPOS, we know that even simple changes based on data, such as adding additional payment options or tapping into cross-border e-commerce, can help cash flow and protect against market shocks. The goal of technology is to mitigate specific challenges without adding layers of complexity.

Investing in Employee Development

Your people are pivotal to your ability to grow as a business; empowered teams are the engine of sustainable scale. A team that feels supported and motivated will bring fresh ideas, adapt to challenges, and push the business forward. Investing in training, mentoring, and development opportunities builds skills that pay back in the form of innovation and improved performance.

In fast-changing industries, having employees who are confident in learning and adapting can make the difference between struggling through disruption and taking advantage of it. Equally, strong partnerships extend this resilience beyond the organisation. Building resilience at the team level creates resilience for the whole business, so fostering a culture of continuous learning and celebrating employee contributions is key to maintaining motivation.

Focusing on Financial Health and Flexibility

Financial resilience underpins sustainable growth. Scaling often requires upfront investment, and without healthy cash flow or reserves, opportunities can be lost. Monitoring income and expenses closely, cutting unnecessary costs, and preparing for seasonal fluctuations gives businesses more control.

Having flexible financing options, like credit lines, small business loans, or even crowdfunding, provides a level of agility. Instead of being caught off guard by unexpected challenges, businesses with financial flexibility are positioned to respond quickly and strategically.

Financial management software can make it easier to track performance, spot issues early, and forecast future needs. When you can see your finances in real time, you can make proactive, data-driven decisions instead of waiting for problems to happen. In markets that change quickly, this kind of financial management helps small firms plan with confidence, stay flexible, and establish a stronger base for long-term growth.

Prioritising Customer Relationships and Feedback

Your customers are not just buyers; they are advocates, sources of insight, and the foundation of repeat business and brand loyalty. Businesses that scale successfully often place customer relationships at the heart of their strategy by actively gathering feedback, responding quickly to issues, and personalising interactions, which shows customers they are valued.

This loyalty becomes a form of resilience. In periods of uncertainty, a base of satisfied, returning customers provides more stability than constantly chasing new ones. Successful businesses use CRM tools to track customer preferences and automate follow-ups so no opportunity to strengthen a relationship is missed.

Building Strategic Partnerships

Partnerships can accelerate growth while also spreading risk. Working with other businesses, organisations, or influencers can provide access to new audiences, shared expertise, or additional resources. Collaboration can also create opportunities for joint marketing, co-branded initiatives, or innovative product and service offerings.

In times of uncertainty, strong partnerships act as a support network. By aligning with others who share your values and vision, you create opportunities that are mutually beneficial and more resilient than going it alone. It is important to find partners whose goals and audiences complement your own for the best long-term impact.

The next stage of small business success will be defined by resilience rather than speed, the ability to adapt, recover, and continue to create value in the fact of uncertainty. For SMEs, this means developing adaptable growth plans that include flexible technology, diverse models and empowered employees.

Learn more at mypos.com

  • Data & AI
  • Digital Payments
  • Digital Strategy
  • Fintech & Insurtech

Author

Dan Brightmore

Published

16 January 2026

Estimated Read time

5Mins

Ben Goldin, Founder and CEO of Plumery, explores the key banking trends for 2026 – from fraud and digital assets to stablecoins and AI applications

As we head into the second half of the decade, several emerging trends will come to the fore in 2026. The interconnectedness among these trends is also noteworthy. Artificial intelligence (AI) and progressive modernisation act as common threads.

A strong current throughout 2026 is the shift from customer-first banking to human-first banking. This relates to the concept of ethical banking. It focuses on creating financial services that have a positive social and environmental impact. 

Human-first banking aims to get even closer to the customer by understanding their actual human needs, rather than just consumer needs. For example, a bank should be acting as a coach to improve a customer’s financial health, not solely as an advisor on which products they should buy. Banks can build trust in a digital world through tailored and empathetic interactions, effectively simulating the experience customers formerly had with their personal banker.

To attain that level of hyper-personalisation, banks will need to be capable of processing vast amounts of transactional data, which can only be accomplished by deploying AI and big data tools. This requirement, in turn, will turbocharge progressive modernisation, another trend that has been bubbling under the surface for the past few years.

Traditional banks are using progressive modernisation to deal with legacy infrastructure that is not fit for purpose in a digital-first, AI-driven world. Instead of a big bang replacement of core banking systems, which is risky and can take years, banks are creating change from within existing architecture. Banking is leveraging technologies that support a multi-core strategy. With this approach, banks can add new cores for specific products that require greater agility and innovation. Modern cores are necessary for deploying the latest AI and big data tools because they provide a unified, real-time data foundation to deliver hyper-personalisation.

Fraud Threats

Fraud will remain a top concern throughout 2026. Adversaries use AI to expand the range of techniques, such as impersonation scams and identity theft, as well as accelerate and scale fraudulent activity.

According to the UK Finance Half Year Fraud Report 2025, £629.3 million was stolen by criminals in the first six months of this year, and there were 2.09 million confirmed cases across both authorised and unauthorised fraud. Card not present cases rose 22% to 1.65 million and accounted for 58% of all unauthorised fraud losses.

However, the good news is that there was a 21% increase in prevented card fraud in the first half of 2025. The £682 million which was stopped from being stolen is the highest-ever figure reported.

To combat fraud, new and improved tools to help banks identify, verify and onboard customers will come to market in 2026. The move away from paper-based identity (ID) and widespread adoption of digital ID will play a key role in the fight against fraud. Hence the UK government’s recently announced plans to roll out a new digital ID scheme.

In addition, I expect to see a fundamental shift in fraud detection using real-time behavioural analytics, data analytics for proactive risk identification, and other applications of AI and machine learning in this space.

Digital Assets and Stablecoins

Digital ID verification is also essential for fighting fraud in the digital assets and stablecoins space. Another hot topic at several banking and payments industry conferences last year.   

In 2026, digital assets and stablecoins will become much more mainstream. Banks have left the sidelines and are now actively engaged with running pilots. For example, in September a consortium of nine European banks, including CaixaBank, ING and UniCredit, announced an initiative to launch a euro-denominated stablecoin.

Central banks and regulators are developing a comprehensive agenda for digital assets. Banks will need to blend traditional fiat currencies and assets with their digital counterparts. This trend is also driving a progressive modernisation approach, as legacy core banking systems weren’t designed to manage digital assets, nor do they support moving money via blockchain-based rails. I expect to see more banks looking to deploy a multi-core strategy where digital assets are managed and stored elsewhere, but they can still provide a seamless and unified experience to customers.

AI

Last year, I predicted that the industry would adopt a ‘meet-in-the-middle’ approach to AI, with banks beginning to uncover the real value that the technology can deliver. I also predicted consolidation, recalibration and stabilisation in the market.

GenAI Banking Applications

My predictions held true, by and large. In 2025, institutions explored what is possible, relevant and achievable within the banking context, then specifically for each individual institution within its legacy architectures and technological environments.

This trend will evolve into more practical actions and initiatives over the next 12 months to provide greater clarity around where GenAI shines versus where it’s not applicable.

To gain clarity, it’s important to understand the difference between AI and GenAI. The latter is built on stochastic principles, which uses probability to model systems that appear to vary in a random manner. This means that the same input could potentially generate different outputs – this isn’t acceptable for automated financial operations, which requires much more determinism. Hence, I believe that GenAI will be used chiefly in scenarios where there’s human intervention.

One area where GenAI is applicable is in conversational applications. For example, banks will begin launching more interactive user interfaces. Customers will be able to interact with the bank as they would a human. Moving beyond simple, frequently asked questions to actual actions.

GenAI in the Back Office

Similarly in the back office, banks can leverage GenAI to provide guidance to their employees and accelerate certain tasks. Using the technology to improve efficiency and help staff do more will have a positive impact on customer experience. Processes will take much less time.

It will also help to bring unbanked segments or non-standard customers, which are difficult and costly to onboard because they require a bespoke assessment, into regulated financial services. Applying GenAI can make the bespoke process much more efficient by providing data-driven insights to support faster and smarter decision-making. This will make it much cheaper to serve these segments. Including smaller and medium-sized enterprises, which will drive financial inclusion and improve customers’ financial health.

Learn more at plumery.com

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Cybersecurity in FinTech
  • Digital Strategy
  • Fintech & Insurtech
  • InsurTech

Author

Can Taner

Published

13 January 2026

Estimated Read time

3Mins

Can Taner, Chief Product Officer at Bitpace, analyses the most important shifts in the crypto and payments landscape

The crypto industry has entered a phase of unbundling. Instead of one-size-fits-all platforms that try to do everything, businesses are looking to specialised providers that solve real-world problems with focus and precision. This shift defines how leading firms now build products: client-first, agile, and compliance-ready by design.

Solving Real Problems with Real Products

The key to building effective crypto payment solutions is understanding what businesses actually need. Payments should help companies operate faster, more efficiently, and at lower cost. Rather than chasing every trend, the focus should be on creating tools that remove friction and add measurable value.

That’s why many providers now offer modular solutions designed to work seamlessly across industries:

  • Payment gateway – enabling merchants to accept crypto securely, with instant conversion to fiat if needed, reducing volatility risk.
  • Global settlements – allowing businesses to move funds cross-border quickly and cost-effectively, bypassing traditional bottlenecks.
  • API integration –giving partners the tools to embed crypto payment functions directly into their platforms, delivering a frictionless experience for end-users.
  • OTC services –providing access to large-scale crypto trades, executed with discretion, high liquidity, and competitive pricing.

Each product is tailored to solve a specific pain point. Instead of bundling everything into a rigid system, we focus on flexible modules that businesses can adopt individually or together.

Agility and Expertise in Product Development

For providers, being specialised also means being agile. Every client problem requires a different approach, and in-house expertise allows them to respond quickly without compromising quality. From compliance to sales to product development, teams must collaborate to find creative solutions that meet the highest regulatory and technical standards.

This agility is only possible if they invest in deep domain knowledge. Product and engineering teams that understand the nuances of payments, crypto, and regulation can adapt quickly to market changes while keeping compliance at the core of every decision.

How to Launch New Products Effectively

Launching a new product in crypto, or any fast-evolving sector, demands structure and discipline. The most successful teams follow a process that balances creativity with rigour.

  • Start with ideation. Listen closely to client feedback, analyse emerging trends, and identify where the market still falls short. Great products don’t begin with technology, but with a clear problem to solve.
  • Do the research. Test assumptions early, model potential use cases, and validate compliance requirements before writing a single line of code. A strong evidence base prevents costly pivots later.
  • Plan collaboratively. Bring product, legal, compliance, sales, and technology teams together from the outset. Aligning goals across functions ensures that innovation doesn’t come at the expense of security or scalability.
  • Build with resilience in mind. Security, interoperability, and performance should be built into the product from day one, not retrofitted at the end.
  • Test thoroughly. Create safe environments to simulate real-world conditions and identify weaknesses before launch. Testing isn’t just a single step, but an ongoing cycle.
  • Launch deliberately. Roll out in phases, gather user feedback, and support early adopters closely. A careful launch builds trust and sets the stage for sustainable growth.

Each of these stages is designed to reduce risk, accelerate learning, and maximise long-term value, principles that define successful product development in today’s crypto landscape.

How Specialisation Wins

Launching products in crypto is about precision and collaboration. The great unbundling of crypto is rewarding those who specialise, focusing on solutions that solve real business challenges. Specialised providers win because they put the client first. That focus on expertise and flexibility is what defines success in the new era of crypto payments.

Learn more at bitpace.com

  • Blockchain & Crypto
  • Digital Payments
  • Fintech & Insurtech

Author

Ben Francis

Published

9 January 2026

Estimated Read time

4Mins

Ben Francis, Insurance Lead at Risk Ledger, on navigating cyber threats by reinforcing security from the inside out

Cyber insurance has evolved from a straightforward risk transfer mechanism into an integral component of enterprise risk strategy. As a result, the conversation has shifted beyond simply securing coverage to embracing three foundational elements: transparency in risk exposure, accountability for security measures, and active collaboration throughout the digital ecosystem.

Rather than asking ‘are you covered?’, the more pertinent question has become ‘can you demonstrate measurable risk reduction?’. Insurers and insureds alike are recognising that what matters now is how well an organisation understands and manages its digital exposure, especially across its extended supply chain. Recent data reveals that 46% of organisations experienced at least two separate supply chain-related cyber incidents in the past year, a clear sign that exposure often lies beyond direct control. 

From Risk Transfer to Risk Visibility 

In recent years, the cyber insurance market has matured significantly. Once viewed as a reactive safety net to cushion the financial impact of attacks, it is now becoming a proactive tool for managing and mitigating risk. This shift is partly driven by insurers, who increasingly expect and work with organisations to demonstrate strong security practices and a nuanced understanding of their threat landscape, including risks deep within their digital supply chains; an area where many businesses still fall short.

At the same time, the industry faces a growing challenge from systemic cyber risk within their portfolios, as many businesses rely on the same cloud providers, payment systems and digital platforms, increasing the chance of a single point of failure. Insurers must gain visibility into how policyholders are connected, not only to suppliers but to each other. Tools and frameworks that map and monitor these interconnections will be essential to avoid underestimating the wider impact of seemingly isolated cyber events.

Mapping Beyond Third Parties

It is no secret that cyber attackers often target the weakest link in a supply chain. These are not always direct suppliers, but fourth, fifth or even sixth-tier vendors that have indirect but critical access to systems and data. Unfortunately, many organisations lack visibility beyond their first tier, creating blind spots that attackers can easily exploit. From an insurance perspective, this presents a clear challenge. If an organisation cannot account for who it is connected to, it cannot adequately quantify its risk and neither can its insurer. Mapping these extended connections is more than just a technical exercise; it means actively practiced risk governance and responsibility. Insurers increasingly want to know how their policyholders are identifying and managing indirect dependencies, particularly in sectors like financial services and retail where disruption can ripple across entire markets.

Collaboration as a Risk Strategy 

One of the more underappreciated aspects of cyber resilience is the role of peer collaboration. Unlike physical incidents, cyber threats rarely exist in isolation. A single compromised vendor can impact multiple organisations simultaneously, a fact that has been highlighted by high-profile supply chain attacks such as SolarWinds and MOVEit. 

As a result, businesses need to think beyond their own perimeters and adopt a more collective mindset. This includes building relationships with industry peers, sharing threat intelligence and participating in sector-wide initiatives aimed at improving visibility and preparedness. 

In highly regulated sectors, such as insurance, this collaboration is increasingly being encouraged by oversight bodies. Frameworks like the Digital Operational Resilience Act (DORA) in the EU and initiatives from the Prudential Regulation Authority (PRA) and the Financial Conduct Authority (FCA) in the UK are pushing for more transparency around third-party risk. In this context, openness is no longer optional; it will be a regulatory expectation. 

For insurance providers, greater collaboration between policyholders also means better data on emerging threats and more accurate portfolio management. For businesses, it offers a chance to anticipate vulnerabilities that may not yet have hit their own networks but are affecting others in their industry. 

Proactive Transparency Builds Trust 

Organisations that take a proactive, transparent approach to cyber risk management are more likely to secure cover and potentially favourable terms, not just in terms of premiums, but also in access to additional services such as forensic support, incident response sources and legal counsel. 

Demonstrating a mature cyber posture is not about claiming perfection. No organisation is immune to breaches. What insurers are looking for is evidence of a structured approach: the existence of incident response plans, robust governance, effective supply chain risk management, and above all, an honest view of risk. 

A Shift in Mindset 

Ultimately, our understanding of cyber insurance must keep evolving. It should not be treated as a simple checkbox exercise, but as a collaborative relationship between insurers and the organisations they support – one built on shared insight, clear communication, and a drive for continuous improvement.

The organisations best equipped to navigate today’s threats will be those that prioritise transparency. Not only does it lead to stronger protection, but it also builds a culture of accountability that reinforces security from the inside out.

Learn more at riskledger.com

  • Cybersecurity
  • Cybersecurity in FinTech
  • Digital Strategy
  • Fintech & Insurtech
  • InsurTech