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Article
Why AI Model Risk Needs a New Rulebook
Financial institutions are struggling to fit rapidly evolving AI into traditional model risk frameworks. Risk leaders warn that interconnected agents, dynamic data, third-party models and continuous change require a shift toward system-level governance, automated monitoring and proportionate oversight without sacrificing human judgment.
Sep 09, 2026
Center for Financial Professionals ,
Tags:
Model risk
Resilience
The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization
- AI should
increasingly be governed as an interconnected system rather than isolated
models
- Common data and
technologies could create hidden dependencies between AI agents
- Dynamic data
governance and output monitoring are becoming essential
- Institutions must
balance AI innovation against uncertainty and model risk
- Vendor AI requires
controlled testing before access to sensitive data or systems
- Traditional periodic
monitoring may be too slow for rapidly evolving AI
- Automated systems may
increasingly monitor other AI systems
- Human expertise
remains essential but must be deployed selectively
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