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Article
Get Model Risk Right and Win the AI Future
Model risk management is not a back office chore but a strategic capability. With AI accelerating complexity and third party dependencies reshaping accountability, institutions that keep governance, documentation, testing, and explainability sharp will outperform. As one senior executive argues, fundamentals matter most - and complacency is costly.
Jan 28, 2026
Center for Financial Professionals ,
Tags:
Model risk
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
Robust MRM frameworks enable resilience during crises and technology shifts like AI
Ongoing vigilance prevents complacency and systemic weaknesses
Common issues include poor documentation inadequate testing and weak validation
Continuous monitoring stress testing and reviews proportional to model materiality are essential
Clear governance structures empowered oversight and strong internal audit strengthen control
Third party models require institution specific validation transparency and mitigating measures
AI should be managed within the modeling risk spectrum with consistent governance
Data governance explainability and proportionate mitigations are vital for AI risk
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