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

A trading desk deploys a deep neural network to generate execution signals. The model performs well in testing, but the risk committee cannot easily trace how individual inputs lead to specific outputs. Which model risk concern does this most directly describe?

The concern is lack of explainability, the black-box problem. Complex AI models such as deep neural networks can perform well yet make it hard for validators and risk committees to see how inputs drive outputs, which weakens model validation, challenge, and accountability.

  1. ALack of explainability (the black-box problem)Correct
  2. BLook-ahead bias in the data sample
  3. CSurvivorship bias in the benchmark index
  4. DBasis risk between hedge and exposure

Explanation

Complex machine-learning models such as deep neural networks often produce outputs whose drivers are hard to interpret. This makes validation, challenge and governance difficult. The other options describe data-sample biases or hedging mismatches, not the inability to trace inputs to outputs.

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