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

A bank uses a deep neural network to detect suspicious trading patterns. Regulators ask the risk function to explain why specific orders were flagged. Which approach most directly addresses this model risk concern while retaining the model's predictive capability?

The bank should apply post-hoc explainability techniques, such as feature attribution, and include them in model validation and documentation. This gives regulators reasons for each flag while keeping the model's predictive power. Removing human review, adding complexity or narrowing training data would not address explainability.

  1. AApply post-hoc explainability tools such as feature attribution and document them within model validationCorrect
  2. BRemove all human review so the model output is the sole basis for decisions
  3. CIncrease the number of network layers to improve accuracy
  4. DRetrain the model only on data from the most recent day

Explanation

Opaque models create explainability and validation challenges. Feature-attribution style tools, documented in validation, give interpretable reasons for flags. Removing human oversight worsens governance, and added complexity or narrow data reduces transparency or robustness.

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