For many UK fintech founders, the word “regulation” feels like a brick wall. In the race to deploy large language models and agentic AI across lending, payments and wealth platforms, there is a pervasive fear that strict governance will drain limited capital and let less-regulated global competitors sprint ahead.
Deepali Limaye Davalbhakta, managing director at Brillio, argues that building AI governance in from day one gives UK fintechs a trust advantage.
Viewing regulation as a barrier is a strategic mistake. The UKs evolving AI landscape offers a genuine competitive edge for fintechs that lean into compliance early, provided founders understand not only what the Financial Conduct Authority (FCA) expects, but also how that compliance travels as they scale into Europe, the Middle East and Asia.
Trust is becoming the primary currency
The anxiety among fintech founders usually stems from the belief that governance requires massive legal teams and slows product cycles. As the market matures, trust is becoming the primary currency instead.
Enterprise banking partners and institutional investors are no longer just asking what the AI can do. They are asking how it was built, where the training data came from and how the organisation mitigates bias in a credit model. Regulation is the framework for building a product that is actually bank-ready.
Building compliant-by-design
Compliant-by-design does not mean hiring a chief compliance officer on day one. It is about treating governance as a product requirement, built into the architecture from the first commit. Compliance stops being a checkbox added before an audit and becomes part of how the system is designed.
For a lean fintech, that means embedding three pillars into the development sprint:
- Data provenance. Document the origin and rights of training data used in underwriting or fraud models from the first line of code.
- Risk tiering. Flag FCA-regulated activities, such as credit scoring, affordability checks and algorithmic trading signals, as high-risk and build in human-in-the-loop overrides before launch.
- Explainability. A credit decisioning model’s outputs should be interpretable enough to satisfy an FCA auditor or a sceptical enterprise client, not opaque by default.
These three pillars are a strong foundation, but they do not operate in a vacuum. How much they actually protect a fintech depends on which regulatory regime it is building against.
The UK approach versus the EU AI Act
Founders must navigate a dual reality. The EU AI Act is a prescriptive, horizontal law with heavy fines for non-compliance, while the UK's pro-innovation approach is currently sector-led and principles-based, empowering existing regulators such as the FCA and the Information Commissioner’s Office (ICO) to manage AI within financial services specifically.
For a UK-founded fintech, this means more flexibility at home. It also means staying alert to FCA guidance rather than waiting for a single all-encompassing AI law.
That flexibility narrows the moment a fintech expands into the EU. A firm compliant under the UK’s principles-based regime can still face the EU AI Act’s prescriptive documentation and conformity-
assessment requirements for any “high-risk” credit or insurance model sold into European markets. That dual compliance burden is already reshaping how UK fintechs sequence their international rollouts.
A global lens: MENA and Asia-Pacific
The UK is not alone in wrestling with this balance. Regulators across MENA and Asia-Pacific are moving on parallel but distinct tracks. The UAE’s Central Bank has issued specific guidance on AI use in financial services risk management, while Singapore’s Monetary Authority has taken a fairness-and-accountability approach through its Veritas initiative, giving fintechs there a model closer to the UK’s principles-based style than the EU's.
For UK fintechs eyeing expansion into these corridors, that convergence is an advantage. A compliant-by-design foundation built for FCA scrutiny travels reasonably well into Singapore’s framework, considerably better than it travels into Brussels.
Scale now, not later
Scaling AI with governance in mind from the start avoids the re-engineering debt that comes later. That debt is not abstract. It looks like retraining a credit model because the original training data was never documented, or building an audit trail retroactively because a regulator wants to see decision logic that was never captured.
It is far cheaper to build a transparent credit model now than to deconstruct a black box once a fintech hits Series B and an institutional investor's due diligence team starts asking hard questions. By then, the model is embedded in live underwriting or fraud detection, and every fix competes with production risk.
Fintechs that treat data provenance, risk tiering and explainability as day-one requirements rarely face this trade-off. The ones that treat them as a later clean-up exercise usually discover the cost at the worst possible moment: mid-raise, mid-audit or mid-expansion into a new regulatory market.
Lead before the rules are set
If that cost curve is the argument for building governance early, the starting point is simple: do not wait for the legislation to be finished before acting. Start by creating an internal AI ethics charter that defines what the company will and will not do with customer financial data.
This documentation serves as a north star for engineering teams and a badge of credibility for investors and banking partners alike. By the time the rules are set in stone, an early mover will already be leading the market.
Early adopters of ethical AI practices in financial services gain a competitive advantage through responsible innovation that builds institutional trust while unlocking business value. Fintechs that delay addressing compliance face steeper costs as regulatory expectations sharpen across the UK, EU, MENA and Asia-Pacific alike.
About the author
Deepali Limaye Davalbhakta is a Managing Director at Brillio, with over 22 years of experience partnering with CXOs to turn AI ambition into enterprise-scale impact. Across the UK and international markets, she has led transformation programmes that unlock growth, modernise technology estates, strengthen regulatory resilience and deliver measurable business outcomes in banking, financial services and insurance.
The post Why UK Fintechs Should Stop Fearing AI Regulation appeared first on The Fintech Times.
