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FRM Part II · FRM Exam Part II · Advances in Artificial Intelligence: Implications for Capital Markets Activities

A bank trains a machine-learning credit-trading model on ten years of market data in which volatility was persistently low. During a sudden regime shift, the model's forecasts deteriorate sharply. Which risk is most clearly illustrated?

The risk is model drift, or poor out-of-sample generalization. A model trained on a low-volatility history has not seen stressed regimes, so when conditions change its learned relationships break down and forecasts deteriorate. This is why monitoring and retraining are needed.

  1. AModel drift or poor out-of-sample generalization when data conditions changeCorrect
  2. BSettlement risk from delayed trade confirmation
  3. CLiquidity coverage ratio breach
  4. DInterest rate risk in the banking book

Explanation

AI models learn patterns from training data and may not generalize to regimes absent from that data. The shift in volatility exposes this weakness, called model drift or non-stationarity. The other options are unrelated to the cause of the forecast failure.

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