Model Risk Management (MRM)

Model Risk Management (MRM) involves identifying, assessing, and mitigating risks associated with the use of financial models, especially those powered by AI and machine learning. Financial institutions rely heavily on models for valuation, risk assessment, and regulatory compliance, making the accuracy and robustness of these models critical.

AI models, particularly those that evolve or “learn” over time, introduce new complexities to MRM. Unlike traditional models with fixed rules, machine learning models can change based on new data, making them harder to validate and monitor. This creates the risk of model drift, where a model’s predictive performance deteriorates over time.

To manage model risk, firms implement governance frameworks that include independent validation, stress testing, and ongoing performance monitoring. Regulators, such as the Federal Reserve, require banks to have robust MRM practices, especially under regulations like SR 11-7, which outlines supervisory expectations for model risk management in large financial institutions.

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