FCA oversight, model risk frameworks, Consumer Duty obligations. You can't retrofit governance after the fact. The organisations getting AI right in financial services are designing the governance in before they deploy anything — and seeing measurable results when they do.
Financial services operates within a regulatory framework that most sectors don't face. That framework exists for good reason — but it means AI adoption here is fundamentally different from AI adoption elsewhere.
The FCA, PRA, and Consumer Duty regulations create hard constraints on what you can deploy and how you deploy it. Model risk requirements mean AI decisions need to be auditable, explainable, and defensible under regulatory scrutiny. Your internal risk and compliance functions need to approve anything deployed in a customer-facing or decision-making context. That's not an obstacle — it's the foundation for AI adoption that actually works.
Meanwhile, fintech and AI-native challengers are moving faster. Legacy technology debt affects data quality, which affects model quality. Shadow AI is spreading across teams without governance oversight. And most financial services organisations haven't yet hired people who understand both the AI and the regulatory dimensions well enough to bridge the gap.
The EU AI Act introduces new classification requirements for AI systems used in financial services. DORA strengthens operational resilience expectations. Existing model risk frameworks weren't designed for generative AI. These regulatory pressures are converging at the same time that competitive pressure to adopt AI is intensifying.
The organisations that get this right won't be the ones that moved fastest. They'll be the ones that built the governance foundation first, then moved with confidence.
Mike led enterprise-wide AI transformation at Verimatrix — a publicly listed global SaaS company — under direct ExCom oversight. Not advising on AI adoption. Executing it across a nine-country organisation with board-level accountability.
This included designing and implementing Responsible AI governance that was institutionalised across a regulated EU-listed company. Converting uncontrolled shadow AI into governed enterprise-wide adoption under an AI Steering Group. Aligning board, technology, legal and commercial stakeholders around accountable AI deployment.
The results were measurable — not theoretical. Engineering productivity, sales effectiveness, customer support efficiency, and significant cost avoidance. All achieved within a governance framework built for regulatory scrutiny.
AI creates value in financial services in distinct areas. Where you start depends on your regulatory constraints, data quality, and which governance questions need resolving first.
Regulatory update analysis, policy gap assessment, compliance questionnaire support, audit evidence preparation. These are high-volume, document-intensive workflows where AI assistance delivers significant time savings with manageable governance requirements. Many financial institutions find this is the fastest path to measurable ROI.
Reporting, knowledge management, document processing, research synthesis. Lower regulatory risk, faster to pilot, and the efficiency gains compound quickly across large teams. This is where most successful financial services AI programmes begin — and where the governance precedents get established for higher-stakes deployments.
Underwriting, credit assessment, claims processing, investment research. AI as a decision support tool with human sign-off preserved. Requires careful workflow design and model risk governance, but the productivity and consistency improvements are significant when done correctly.
Communication, routing, query resolution, self-service. Requires Consumer Duty compliance, fairness testing, and clear accountability structures. Higher governance complexity, but meaningful potential for both cost reduction and improved customer experience.
AI coding assistants, technical documentation, debugging support. Many financial institutions are seeing significant productivity gains from governed AI coding tools — while maintaining the code review and security processes their environments require.
Drafting proposals, summarising client requirements, analysing RFP responses. Client-facing teams produce large volumes of documentation where AI assistance can meaningfully improve speed and consistency — particularly in wealth management, corporate banking, and advisory practices.
Every financial institution has different regulatory obligations, different technology landscapes, and different organisational readiness. The engagement always starts with understanding yours.
Whether you're a bank exploring internal productivity use cases, an insurer looking at claims processing, or a wealth manager considering AI-assisted advisory workflows — the approach is structured around your governance reality, not a generic playbook.
A focused 2–4 week assessment that maps where AI can genuinely improve productivity or reduce risk, identifies which governance and model risk requirements must be resolved first, and produces a realistic pilot recommendation. The governance risk assessment component is particularly valuable in financial services — it maps which workflows have regulatory implications before any pilot commitment is made.
Most organisations find the honest answer is that they're further from deployment-ready than they thought. The Radar tells them specifically why, and what to resolve — so they invest in the right areas first.
A 6–12 week pilot in a real operational context. Not a sandbox or proof of concept — a genuine working pilot where compliance is designed in from the start. The pilot blueprint defines governance controls, model risk oversight, and human sign-off requirements alongside the workflow design. That makes it easier to scale and easier to get past your risk and compliance function.
A 3–6 month engagement to move from pilot success into operational sustainability. This means defining human-AI accountability structures that regulators can understand, building governance review cycles into normal operations, and ensuring the workforce knows what is and isn't appropriate to delegate to AI. For institutions with multiple business lines, this often means establishing a shared AI operating model with clear escalation routes.
A retained engagement — typically 1–3 days per month — providing senior AI oversight. Especially valuable for financial institutions at the early stages of AI adoption where board-level credibility and regulatory dialogue are becoming necessary, without the cost of a permanent CAIO hire. Mike can engage credibly with boards, regulators, and risk committees — because he's done exactly that in a regulated, publicly listed environment.
Governance-first, regulation-aware AI adoption — from someone who's delivered enterprise AI transformation inside a regulated, publicly listed organisation.