Generative Artificial Intelligence (AI) is transforming the financial industry, yet it also poses unique risks. This blog explores how existing model risk management (MRM) frameworks can be adapted to mitigate these risks in the generative AI era.

Traditional Model Risk Management Frameworks

MRM frameworks, which address risks associated with models used in decision-making, typically encompass:

• Model validation: Assessing accuracy, reliability, and limitations
• Governance: Establishing roles, responsibilities, and oversight processes
• Risk mitigation: Identifying and addressing potential risks, such as model bias and data quality issues

Adapting MRM to Generative AI

Generative AI has unique characteristics that require adaptation of MRM frameworks:

• Potential impact: Generative AI can significantly enhance efficiency and customer engagement, as well as mitigate fraud and security risks.
• Novel capabilities: Generative AI creates new content based on learned patterns, enabling dynamic and creative applications.

Regulatory Considerations

Regulators can support the responsible adoption of generative AI by:

• Recognizing industry best practices and standards as presumptive evidence of MRM compliance
• Providing clarity on documentation expectations for generative AI models
• Taking into account practices like grounding and outcome-based model evaluations
• Recognizing controls such as continuous monitoring and human-in-the-loop oversight

Collaboration and Responsibility

Collaboration among industry participants, regulators, and governments is crucial for responsible innovation in generative AI. By adhering to robust MRM practices, we can harness the transformative potential of this technology while ensuring the safety and soundness of the financial system.