Artificial intelligence promises to streamline financial services, but deploying it in highly regulated markets requires careful governance frameworks that most institutions haven't yet built. A new guidance note from fintech experts highlights the specific risks and opportunities when using AI in capital markets, drawing on real-world experience from withholding tax reclamation and other regulated financial processes.

What Makes AI Governance Different in Finance?

Financial services operate under layers of regulatory oversight that most other industries don't face. When AI systems make decisions about client approvals, risk assessments, or entitlements, regulators and clients alike need to understand how those decisions were made. This requirement for explainability, combined with the potential scale of harm from AI mistakes, creates a governance challenge that goes well beyond typical software deployment.

The challenge becomes even more complex when financial processes span multiple countries and jurisdictions. Data quality issues emerge when information comes from different sources with different standards, regulatory regimes change at different speeds across regions, and privacy requirements vary by location. These aren't theoretical concerns; they're practical obstacles that institutions encounter when trying to implement AI at scale.

"Many financial markets processes, many of which are in the throes of regulatory digitalization, still present challenges for effective AI deployment," noted Austen Little, Head of Product Development at TaxTec, highlighting areas including data quality issues, changing regulatory requirements, explainability demands, and the nature and scale of potential harms from AI agent mistakes.

Austen Little, Head of Product Development at TaxTec

Where Does AI Actually Work Well in Finance?

The governance challenge doesn't mean financial firms should avoid AI entirely. Rather, institutions need to match AI capabilities to specific use cases where the technology can deliver value without creating unmanageable risk. Certain applications have proven safer and more effective than others in regulated financial environments.

Document management, data extraction, and data quality enhancement represent areas where AI can operate with clear boundaries and measurable outcomes. These tasks involve processing information rather than making high-stakes decisions about clients or risk. Similarly, AI excels at research assistance, workflow management, status monitoring, and analytics and forecasting, where the technology supports human decision-makers rather than replacing them.

Steps to Building Effective AI Governance in Financial Services

• Data Quality Assessment: Audit data sources across all jurisdictions where your firm operates to identify quality gaps, inconsistencies, and compliance issues before deploying AI systems that depend on clean data.
• Explainability Requirements: Design AI systems with built-in transparency so that client decisions, risk approvals, and entitlements can be explained to regulators and clients in plain language, not just as model outputs.
• Risk Scoping: Evaluate the potential scale and impact of AI mistakes in your specific use case; high-stakes decisions affecting client entitlements or regulatory compliance require more rigorous governance than supporting tasks.
• Regulatory Monitoring: Establish processes to track changing regulatory requirements across all jurisdictions where your firm operates, since AI governance frameworks must adapt as rules evolve.
• Use Case Prioritization: Start with lower-risk applications like document processing and data extraction before moving to decision-making systems, allowing your governance framework to mature alongside your AI deployment.

Why This Matters Now

Financial institutions face pressure to modernize quickly, and AI offers genuine efficiency gains. However, the tension between speed and compliance creates real governance risks. Regulators worldwide, including the OECD (Organization for Economic Cooperation and Development), the World Economic Forum, and the International Monetary Fund, have all issued guidance urging caution about machine learning in financial services processes and decision-making.

The stakes are high. A poorly governed AI system in a capital markets context could expose a firm to regulatory penalties, client disputes, and reputational damage. Conversely, institutions that build robust governance frameworks early can deploy AI more confidently and capture competitive advantages in efficiency and service quality. The key is matching governance rigor to the actual risk profile of each use case, rather than applying blanket restrictions or, conversely, deploying AI without adequate safeguards.

As financial markets continue their digital transformation, the institutions that succeed will be those that treat AI governance not as a compliance checkbox but as a core part of their product development strategy.