2026 FinTech Predictions: Insights from Andy Davies of Endava

I spoke with Andy, Senior Global Payments Specialist at Endava, a technology-driven business transformation group helping businesses innovate with AI and technology to solve complex challenges.

Andy shares his perspective on how AI will become structurally critical in 2026, the evolution of API platforms, and why traditional risk models may struggle to keep pace with always-on systems.

Over to you Andy - my questions are in bold:

What's the biggest shift you expect across financial services in 2026?

The biggest shift we expect in 2026 is the industry's move from experimentation to production for their selective Generative and Agentic AI initiatives, not driven by novelty but by business necessity. Regulation, customer expectations and platform-led distribution models are pushing financial services towards continuous assurance and always-on operating models, rather than periodic checks or one-off interactions.

In this environment, AI becomes structurally important, and critical. Not as an enhancement layered onto digital transformation, but as a foundational capability embedded into core operations. Ongoing monitoring, faster escalation, defensible audit trails and real-time decision-making are quickly becoming table stakes. The approach from Institutions to compliance, risk and customer experience through strong data management and clearly defined workflows will see their core services evolve into live operating systems.

In 2026, success will depend on building trusted foundations with clear guardrails across payments, fraud and disputes, allowing innovation to scale safely. AI will increasingly prioritise, streamline and surface what matters most. Human teams will focus on strategic judgement rather than operational firefighting, building long term value rather than focusing with temporary fixes.

Which emerging technology will have the most practical impact on banks and the FinTechs that support them?

The most practical impact will come from applied AI built on mature data and platform foundations, alongside the evolution of API platforms and operating models within banks. Machine learning, graph analytics, decision engines and large language models are already delivering value, but their real impact comes when they are deployed in focused, operationally embedded ways.

Banks are being forced to rethink how APIs are governed and operated. As ecosystems expand, effective API governance, partner engagement and the emergence of agentic API support models will become critical. APIs are no longer just technical interfaces; they are commercial products needing to scale securely, reliably and transparently across complex partner networks. This commercialisation of consumption change is reflected with the increasing switch to data and AI as products.

Together, AI and modern API platforms are transforming areas such as KYC, AML, underwriting, credit assessment and customer support. The institutions investing now in clean data, automated operations and API maturity will see AI move decisively from experimentation into dependable value creation and revenue generation.

What customer behaviours or expectations will most challenge banks and financial service providers?

Customers increasingly consume financial services through secondary platforms rather than directly from banks. Payments, lending and financial tools are being embedded into the software people already use, such as booking systems, ecommerce platforms, marketplaces and enterprise tools, shifting ownership of the customer relationship away from traditional providers.

At the same time, both retail and business customers expect richer digital interactions, clearer information and intuitive self-service to support financial decision-making. They want unified dashboards, contextual insight and instant activation, not fragmented journeys across disconnected systems.

Meeting these expectations requires a true unified view of the customer, which remains a significant challenge for many institutions still constrained by siloed data and legacy architectures. Addressing this means opening up the sources of data internally and externally, modernising the data platforms services and the related operating models, so providers can support complex workflows at scale. AI will increasingly support reconciliation, dispute handling and service responsiveness, but only where the underlying data foundations are fit for purpose.

What risks or blind spots do you think the industry is underestimating as we move into 2026?

One major blind spot is the assumption, traditional risk and control models will scale effectively into more automated, AI-driven environments. Many existing approaches still rely heavily on human oversight and direct line-of-sight controls, which struggle to keep pace with real-time, always-on systems.

There is a growing need to reimagine how risk and controls operate, using technology not just to replicate existing processes, but to fundamentally redesign them. New tools can strengthen assurance, but they also challenge established norms around accountability, explainability and governance. Developing a genuine AI first approach.

Existing data platforms will face an existential mismatch with the massive increase in query volumes and complexity caused shifting to agentic AI. This means the platforms and data must be AI Agent-ready to help address increased workload demands.

Another underestimated risk is over-simplifying innovation narratives, such as assuming instant settlement alone will transform payments. In reality, cost, cross-border complexity and operational readiness still matter. BNPL illustrates this evolution well: the market is maturing, with greater emphasis on affordability, orchestration and platform-led models, where AI plays a central role in improving risk signals and outcomes for all parties

If you were advising a bank's leadership team today, what strategic priority should they focus on to stay competitive in 2026 and beyond?

The priority should be AI readiness, underpinned by relevance within the customer's daily financial workflow. Payments are no longer just infrastructure; they are the gateway to data, insight and long-term engagement.

This starts with modernising data architectures so they can support the rapidly evolving demands of AI adoption across the business with real time access to quality assured traceable data and insight. It also requires the maturation of 'API factory' capabilities to manage the explosion of open connectivity across banking ecosystems, ensuring APIs are secure, scalable and commercially viable.

Equally important is investing in people, and developing the skills, understanding and operating models required to apply AI responsibly, with the ability to adapt to rapid innovation and automation, and integrate the means to scale at pace. As the lines between banks, fintech's and platforms continue to blur, competitive advantage will come from delivering seamless, trusted experiences embedded directly into the flow of commerce. Success will ultimately be defined by who makes financial services feel seamless, reliable and genuinely helpful, becoming a core part of how consumers live their lives and run their businesses.

Thank you Andy! You can connect with Andy on his LinkedIn Profile and find out more about the company at www.endava.com.