This year’s agenda brings together senior technology leaders from across fintech to share how they’re navigating the realities of the role today.
Across two days, you can learn about how fintech organisations are adopting AI responsibly, managing compliance-led delivery, evolving platforms, and continuing to ship in highly regulated environments.
You will hear practical operating stories from CTOs, VPs Engineering and Heads of Technology on how they are introducing new tooling, restructuring around emerging workflows, and balancing speed with regulatory responsibility.
The Inevitability of Tech Debt
Every fintech carries it. The decisions made at speed in year one, the shortcuts taken to hit a compliance deadline, the architecture that made sense before you scaled.
The question isn’t whether you have tech debt. It’s whether you’re leading it or it’s leading you.
Harry Jell has co-founded and scaled a regulated credit card business from scratch. He’ll share what it actually looks like to manage rewrites and accumulating debt inside a business where regulators, investors, and customers all have a stake in your platform staying stable.
Harry Jell, CTO, Yonder
Building 10x Organisations Using Modern Productivity Metrics
10x developers may be a myth, but 10x organisations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
This session will take a detailed, granular look at the barriers to productivity developers in Fintech face today and modern approaches for removing them.
You’ll learn about the history of productivity theory, and how previous practices have influenced our current understanding. DevOps and Platform Engineering philosophy is clearly converging on developer experience.
But which of these approaches works? DORA? SPACE? DevEx? What should we invest in and create urgency behind today so we don’t have the same discussion again in a decade?
Justin Reock, Deputy CTO, DX
Self-Serve Analytics: Dream. Reality. What Actually Changed.
For a decade, self-serve analytics promised to put data in everyone’s hands.
Then AI arrived and promised to finish the job. But the uncomfortable truth is that neither changes what actually matters: without solid data foundations, you don’t get insights or better decisions, you just get faster access to the wrong answers.
This talk unpacks what the last ten years have actually taught us, what AI genuinely changes and what it doesn’t, and what it really takes to turn data access into decisions that stick.
Varalakshmi Venkatraman, Head of Data Engineering, Marshmallow
The New Bottleneck: Why Verification Is The Real Competitive Edge In AI-Assisted Engineering
AI has solved the wrong problem. Individual developer velocity is up, but company velocity isn’t keeping pace. Teams using AI open roughly twice as many pull requests. Review time climbs. Bugs and production incidents follow.
Writing code was never the constraint. Verifying it was.
Using AI is no longer a differentiator. The teams that pull ahead are the ones who harness that speed without sacrificing correctness. In a regulated finance environment where FCA, PRA, and DORA set the bar on operational resilience, verification isn’t a tax on speed. It’s what lets the whole business move faster.
Leave with a clear argument for where verification deserves investment, and where to start.
Carl Sverre, Field CTO, Antithesis
The Self-Healing Bank: Turning Customer & Operational Signals Into An Agentic Engineering Pipeline – From Raw Signal To Proposed Change
At Xapo Bank, they’re building a single, ambitious capability: every signal, wherever it comes from, flows into one agentic engineering pipeline that turns it into a proposed change – a fix, an improvement, or a better experience.
Find out how they channel signals from two directions into the same automated loop. From customers: app store reviews, Trustpilot, support conversations, feature and UX requests and more, flowing automatically into the engineering backlog and onward into AI-proposed pull requests. From inside the bank: production anomaly detection, analytics and incidents that trigger automated root-cause analysis, proposed changes, and first-draft post-incident reports ready for human sign-off.
The result is a bank that increasingly detects, explains, and improves itself – turning scattered feedback and operational signals into engineering action. They will walk through the architecture behind it, the wins, and the hard lessons from building it for real.
Kamil Dziublinski, CTO, Xapo Bank
The Missing Vault: The Platform Controls Every AI System Needs
A customer reports a suspicious AI response. The investigation finds no breach, no compromised model, and no infrastructure failure, yet something critical is missing.
Through the story of a fictional fintech incident, this session explores the platform controls needed to safely scale AI in regulated environments, introducing the seven “vaults” that protect data, identity, secrets, policies, prompts, decisions, and auditability.
The central idea is simple: most AI incidents aren’t model failures, they’re platform failures.
Swapna Alladi, Head of Platforms, Allica Bank
Panel – AI Wrote the Code. Who’s Checking It Before It Touches Money?
In fintech, code moves customer money and sits in front of regulators. AI now writes a growing share of it, and your engineers have less context on what shipped. Your risk team already asks the obvious question: does that mean more risk in the systems you’re accountable for?
CFOs want proof the AI spend pays off. CEOs want every team using it now, before competitors move first.
They’ll discuss how they validate AI generated code before it reaches a regulated environment, how they measure its value without losing CFO trust, and how they brief the board on AI adoption while staying audit ready.
Peter Donlon, CTO, Zopa Bank
Abiodun Ajibade, CTO, Metrifox
Nkechi Anyanwu, Founding Engineer, Vouchsafe
Holly Jukes, Head of Engineering, Lloyds Banking Group
Breakout Discussion – Creating Your Personal User Guide
You have written documentation for every product you have ever shipped. Yet the people you lead have to reverse-engineer you: how you make decisions, how you like to receive feedback, what you look like under pressure. They usually learn it slowly, and often by getting it wrong first.
A Personal User Guide is the README for you as a leader. Done well, it shrinks the time it takes to build trust with a new report, peer, or stakeholder from months down to a single conversation.
Drawing on years of facilitating human-centred leadership and evaluating thousands of founders as an early-stage investor, Andy shares the exact framework he uses to build trusted relationships quickly. This is not a talk you sit through. You leave with a first draft of your own guide.
Andy Ayim, Director Leadership Development, Ayim Ltd