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FRM Part I · FRM Exam Part I · Enterprise Risk Management and Future Trends

A firm adopts machine-learning models for credit scoring and algorithmic trading. From a risk management perspective, which concern is most directly associated with the 'black box' nature of these models?

The main concern is model risk: black-box machine-learning models have limited interpretability, so validating them, explaining decisions to regulators and detecting bias or overfitting become difficult. Automation does not remove operational risk, and these models make data governance more, not less, important.

  1. AModel risk from limited interpretability, making it hard to validate and explain outputsCorrect
  2. BHigher liquidity risk because ML models always trade illiquid securities
  3. CElimination of operational risk because decisions are automated
  4. DReduced need for data governance since models learn on their own

Explanation

Opaque models are hard to interpret, which complicates validation, explanation to regulators and detection of bias or overfitting; this is model risk. Automation does not remove operational risk, and ML models increase rather than reduce reliance on good data governance. Liquidity risk is not a necessary result of opacity.

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