Review Model Risk Management

Model risk management is the discipline of identifying, assessing, controlling, validating, monitoring, and governing risks created when decisions depend on models.

MRM treats a model as something that can be wrong, misused, misunderstood, or become unsuitable over time. It establishes lifecycle responsibilities and evidence for deciding whether reliance is acceptable.

AI systems can expand model use beyond traditional quantitative settings. Organizations need risk practices that account for both the model and its surrounding workflow.

A bank independently validates a credit model, documents limitations, monitors performance, controls changes, and defines escalation when thresholds are crossed.

Model risk management is not only a one-time accuracy test. It includes intended use, implementation, governance, monitoring, change, and retirement.

Manage the risk of relying on the model across its whole life.

* [[ai-governance|AI Governance]] * [[independent-verification|Independent Verification]] * [[model-drift|Model Drift]] * [[model-card|Model Card]] * [[human-ownership|Human Ownership]]

* [Federal Reserve, SR 26-2: Supervisory Guidance on Model Risk Management](https://www.federalreserve.gov/supervisionreg/srletters/SR2602.htm) — current U.S. Federal Reserve supervisory guidance on model-risk governance, validation, and controls.

* Connect lifecycle controls to [[model-drift|Model Drift]] and [[independent-verification|Independent Verification]].