2026 FinTech Predictions: Insights from Peter Pugh Jones of Confluent

I spoke with Peter Pugh Jones, EMEA Field CDO at Confluent, about the forces that will shape financial services in 2026.

With economic pressure mounting and AI moving from experimentation to execution, Peter shares his view on what will separate leaders from laggards in the year ahead.

Over to you Peter - my questions are in bold:

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

The biggest shift in 2026 will see financial service organisations move from experimentation to accountability.

For a long time, banks and service providers have been able to trial new technologies, pilot AI use cases, and talk about transformation without clearly demonstrating long-term value. But with economic pressure, geopolitical uncertainty, and closer scrutiny of technology all cranking up the pressure, that situation is changing.

As a result, organisations will have to be far more disciplined in their investments. Every initiative needs a clear connection to real-world outcomes, whether that's reducing risk, improving resilience, or better protecting customers.

We'll see businesses move away from surface-level innovation, cultivating core systems that use AI to supercharge day-to-day functions from personalisation to cybersecurity. Those that succeed here will clearly demonstrate themselves as having strong data foundations; those that can't will continue to rely on short-term workarounds.

In 2026, execution and resilience will matter more than who adopts the latest technology first. It will ultimately determine who pulls ahead and who falls behind.

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

The most practical impact will come from real-time data.

Data might be the most important resource an organisation in financial services has. As AI-driven services continue to be normalised, and increasingly expected by consumers, their effectiveness will increasingly depend on how quickly and accurately they can access the data that fuels them.

It's data that enables systems to respond to events as they happen rather than after the fact. It's what underpins real-time fraud detection, scam prevention, and more proactive customer protection — use cases where speed and context matter just as much as intelligence.

The faster you can access, analyse, and action that data, the faster you can make the right decision. We're already seeing this reflected in modern scam-detection tools that combine human input with immediate AI analysis.

In simple terms, AI on its own creates potential; AI paired with streaming data creates outcomes. That's why this combination will have the biggest practical impact across banks and the fintechs that support them.

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

One major blind spot is underestimating the impact of scaling AI activity, and how unprepared organisations are for dealing with the consequences. As AI systems generate and consume increasing volumes of data, weaknesses in data quality, system integration, and governance are exposed very quickly as the pressure on them rises.

Another risk is assuming AI can simply be layered on top of existing infrastructure, usually as a result of small, contained success in small pilots, or shiny front-end tools which make AI appear easier to adopt than it really is. In practice, that often leads to duplicated data, inconsistent decisions, and a lack of trust in AI-driven outcomes.

Finally, some institutions still see regulation purely as a constraint. This is particularly evident in regulated sectors like financial services, where elements of the EU AI Act like the compliance rules around high-risk systems, are often seen as barriers to innovation rather than as frameworks for building trust and scale. In reality, regulation provides the guardrails that make large-scale AI deployment viable. Ignoring that balance is a risk many organisations won't be able to afford in 2026.

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 clarity — not just in terms of data, and the outcomes of AI, but also the responsibility of these things. Leadership teams need to be clear about what they want AI to achieve, whether that's reducing fraud, improving operational efficiency, or protecting customers more effectively, and then work backwards from there.

That means investing in real-time data, addressing legacy systems properly, and putting governance in place that supports innovation rather than stifles it. It also means deploying AI to enhance employee capability and decision-making.

There's also a tendency to treat AI as a technology rollout rather than a cultural and organisational shift. The leadership teams that oversee the implementation of AI are responsible to make that an ideological shift as well as a technological one.

As AI becomes a larger and more visible part of the digital economy, the banks that stay competitive will be the ones that make disciplined technology bets and deploy them responsibly. Trust, scale, execution, and a genuine belief in the cultural impact will all matter just as much as innovation.

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

Given the challenges with data that we've spoken about so far, effective personalisation at scale will be one of the hottest focuses for 2026. It's one that might make or break a relationship with a consumer.

Customers now expect their bank to genuinely know them and reflect that understanding in every interaction. They want products and services that align with their individual preferences and behaviours, and they expect that experience to carry seamlessly across apps, websites and conversations with customer service teams.

Repeating information or being treated like a stranger at each touchpoint is no longer acceptable. The challenge for many banks is that their underlying systems were never designed to support this level of continuity. Legacy technology struggles to connect customer data, leaving customer service stuff and automated systems a fragmented picture with which to deliver on customer expectations.

That leaves banks facing a critical decision. They can invest in modern platforms that support more personalised, joined-up experiences — or continue to lose ground to digital-native providers that built their customer journeys around these expectations from day one.

Thank you Peter! You can connect with Peter on his LinkedIn Profile and find out more about the company at www.confluent.io.