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Model Risk Management is the discipline of identifying, assessing, mitigating, and monitoring risks arising from the use of quantitative models to support business decisions.
Model Risk Management, the discipline of identifying, assessing, mitigating, and monitoring risks arising from the use of quantitative models to support business decisions.
Model risk management (MRM) predates AI governance, the US Federal Reserve's SR 11-7 (2011) was the foundational regulatory guidance, written for statistical and econometric models in banking, and was superseded on 17 April 2026 by the revised interagency guidance SR 26-2. AI systems are a superset of the models SR 11-7 was designed for, and regulators have progressively extended MRM expectations to machine learning. MRM covers the full model lifecycle: development, validation (independent from development), approval, ongoing monitoring, and retirement. The "use it or lose it" principle, understanding why a model produces its outputs, is increasingly strained by complex ML systems.
Source: Federal Reserve SR 26-2 (17 April 2026), superseding SR 11-7 (2011)
Model risk management (MRM) predates AI governance, the US Federal Reserve's SR 11-7 (2011) was the foundational regulatory guidance, written for statistical and econometric models in banking, and was superseded on 17 April 2026 by the revised interagency guidance SR 26-2. AI systems are a superset of the models SR 11-7 was designed for, and regulators have progressively extended MRM expectations to machine learning. MRM covers the full model lifecycle: development, validation (independent from development), approval, ongoing monitoring, and retirement. The "use it or lose it" principle, understanding why a model produces its outputs, is increasingly strained by complex ML systems.
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