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FRM Part II · FRM Exam Part II · Credit Scoring and Retail Credit Risk Management

A bank wants to use a complex machine-learning model for retail credit decisions that outperforms its logistic scorecard but is hard to interpret. Which approach best reflects sound model risk management?

The bank should independently validate the model, test it for bias and stability, document its limitations, and apply explainability tools with ongoing monitoring. Higher accuracy does not remove model risk, complexity is not prohibited, and validating on the development sample alone would be inadequate.

  1. ASubject it to independent validation, test for bias and stability, document limitations, and put in place explainability tools and ongoing monitoringCorrect
  2. BDeploy it immediately because higher predictive accuracy removes model risk
  3. CReject it because any complex model is prohibited under supervisory guidance
  4. DValidate it using only the same development sample to save time

Explanation

Supervisory model risk guidance expects independent validation, conceptual soundness review, outcome analysis and ongoing monitoring, with greater effort for complex models. Accuracy alone does not eliminate risk, complex models are not prohibited, and in-sample validation is inadequate.

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