Move Fast without Breaking the Bank Model Risk Management of GenAI workflows
A new report warns that Generative AI in banking offers major benefits but also serious risks, as systems grow more complex and unpredictable. Existing risk management rules like US SR 11-7 and UK SS 1/23 need major updates. The authors propose a three-pillar strategy: better governance, strict design standards, and advanced testing, including stress tests and red-teaming. Notably, they stress classifying GenAI risks and quantifying uncertainty to avoid costly errors, urging banks to rethink oversight.
What it examines
This report examines how financial institutions can safely use generative AI by updating model risk management practices. It focuses on governance, model design, testing, and monitoring, aligning with regulatory standards to address new risks from complex, dynamic AI systems in banking.
What it concludes
The study highlights that strong model risk management enables banks to benefit from generative AI while controlling risks. Applications include decision-making, risk assessment, and automation. Ongoing testing, monitoring, and clear documentation are essential. Future work should address emerging technical challenges and evolving regulatory expectations as AI use expands.
Evidence objects
Generative AIs rapid rise in banking offers major opportunities but introduces serious risks, demanding urgent updates to traditional risk management frameworks to address unpredictable outputs, external dependencies, and dynamic system changes.
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The report proposes a three-pillar strategy: enhanced governance and risk tiering, rigorous design standards, and advanced testingincluding stress testing and red-teamingto catch rare failures and classify GenAI risks more effectively.
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A standout insight is the emphasis on uncertainty quantification, new GenAI workflow terminology, and continuous monitoring, urging banks to rethink risk management to safely harness GenAIs power and prevent costly mistakes.
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This paper uniquely addresses the emerging risk landscape from generative AI (GenAI) and large language models (LLMs) in finance. It innovatively adapts model risk management frameworks (e.g., SR 11-7, SS 1/23), introduces governance and testing strategies, and highlights novel challenges, making it a timely, compelling, and original contribution.
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Raw abstract and provenance
In summary, managing model risk of AI is necessary for safeguarding the deployment of large, composable, and evolving GenAI workflows at production scale. This
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