Why AI Has Become a Regulatory Priority

Artificial intelligence has moved beyond experimentation in financial services. It now underpins credit assessments, fraud detection, transaction monitoring, customer segmentation, and marketing decisions. As AI becomes embedded in critical business processes, regulators have shifted their focus from innovation potential to governance, accountability, and risk.

Supervisory authorities are increasingly concerned about explainability, bias, data quality, and the potential for systemic risk. AI governance is no longer viewed as a technology issue—it is a core component of enterprise risk management.

Why Traditional Model Risk Frameworks Fall Short

Many institutions rely on existing model risk management (MRM) frameworks to oversee AI. While helpful, these frameworks were designed for static, rules-based models—not adaptive systems that learn and evolve.

AI introduces new challenges:

• Limited transparency in complex models
• Continuous model drift driven by new data
• Embedded bias from historical datasets
• Increased reliance on third-party vendors

Without enhancement, traditional controls struggle to keep pace with AI complexity.

‍

Defining Clear Ownership and Accountability

Effective AI governance begins with clearly defined accountability across the model lifecycle:

• Business owners accountable for outcomes and customer impact
• Developers and data scientists responsible for design and testing
• Risk and compliance teams overseeing controls and approvals
• Senior management setting risk appetite and governance standards

When accountability is unclear, regulatory scrutiny intensifies.