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Model risk refers to the potential for losses or adverse outcomes caused by flawed, biased, or misapplied quantitative models and their outputs. The concept covers model validation, data quality, governance, monitoring and documentation to detect uncertainty, overfitting, performance drift and implementation errors, and to manage model-related business and r…
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Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
Model risk is the danger that wrong assumptions, unsuitable data, faulty implementation, or inappropriate use cause a model to produce poor or harmful decisions. It concerns uncertainty in both the model and its application.
The term was shaped in finance, where statistical and economic models support credit, market, and valuation decisions. Guidance such as SR 11-7 made it a systematic management concern; machine learning added more complex data and behavior risks.
Check model risk at three handoffs: does the model fit the task and data; is it implemented and operated correctly; and is its output interpreted and bounded in the right context? A failure at any handoff can become a bad decision.
Simplifications and prerequisites define the conditions under which a model can produce valid results.
Independent checks examine the concept, data, implementation, and performance boundaries.
A correct prediction can still be risky when used in the wrong process or without suitable human review.
Model risk supports decisions about the checks, controls, and limits required before and during model-supported decision making.
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