AI governance: Building trust and compliance in financial services

The financial services sector faces a unique challenge with AI adoption. While technology promises operational efficiency and enhanced customer outcomes, the stakes are higher than in any other industry. One misstep can result in regulatory breaches, customer harm, and reputational damage that takes years to repair.

This reality demands a fundamentally different approach to AI governance in financial services. Generic models and surface-level safety measures won’t suffice when dealing with vulnerable customers, sensitive financial data, and evolving regulatory requirements. Financial institutions need AI systems with governance built into their foundation from the start.

The Regulatory Landscape Is Complex and Evolving

The regulatory environment has intensified dramatically. FCA fines tripled to £176 million in 2024 (1), while the EU AI Act now imposes fines of up to 7% of annual global turnover for AI non-compliance (2). Meanwhile, 84% of UK financial firms identify “safety, security and robustness of AI models” as their primary constraint to AI adoption. (3) (4).

The EU AI Act classifies financial AI applications as “high-risk,” imposing strict requirements for transparency, accountability, and ongoing monitoring. The FCA has established six core principles for AI use in financial services: transparency, fairness, accountability, security, redress, and data governance. The PRA focuses on operational resilience and model risk management.

Compliance requires firms to demonstrate systematic approaches to AI governance that span the entire model lifecycle, from development to deployment to monitoring. The challenge is building systems that can deliver compliance consistently.

When regulators ask about your AI governance, you have concrete evidence of proactive risk management rather than reactive damage control.

Moving Beyond Surface-Level Compliance

Traditional approaches to AI governance often treat safety as a single dimension. This creates blind spots that prove costly in financial services applications. A model might perform well on standard benchmarks while still exhibiting bias in credit decisions, hallucinating regulatory advice, or failing to protect sensitive customer data.

Effective AI governance requires a risk-specific approach that addresses each category of potential harm:

Bias and fairness: Ensuring equitable treatment across customer demographics
Data protection: Safeguarding personal and financial information
Misinformation: Preventing inaccurate financial guidance or regulatory advice
Transparency: Providing clear explanations for automated decisions
Accountability: Establishing clear ownership and responsibility for AI outcomes

A comprehensive safety framework reduces your exposure to regulatory violations, reputational damage, and operational errors. When regulators ask about your AI governance, you have concrete evidence of proactive risk management rather than reactive damage control.

Each risk category requires dedicated evaluation methods, mitigation strategies, and monitoring systems. A comprehensive governance framework maps these requirements across every stage of the AI development lifecycle.

Practical Governance Architecture That Works

It is important to consider a governance framework specifically designed for financial services AI. For example:

Consider the AI Principles: Establish ethical foundations aligned with FCA principles
Examine regulatory frameworks: Map requirements from EU AI Act, FCA guidelines, and PRA expectations
Assign standards and requirements: Create specific documentation standards for compliance demonstration
Summarise our approach: Maintain transparent reporting on governance implementation
Create individual artefacts: Develop policies, impact assessments, and compliance documentation

A framework such as this ensures clear traceability from high-level principles to specific technical practices, enabling comprehensive auditing and regulatory demonstration.

As we move into an AI-first world it is important that risk and governance frameworks are prioritised from the outset. It is a fast paced transformation rather than a slow-paced evolution and the industry has the opportunity to be on the front foot with clear planning, parameters and principles in place.