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FRM Part II · FRM Exam Part II · The Financial Stability Implications of Artificial Intelligence

A lender introduces a machine learning credit scoring model trained on alternative data, claiming improved default prediction. Which concern about this use case is most directly highlighted in the financial stability discussion of AI in finance?

The main concern is that opaque machine learning models can embed hidden bias and that many lenders using similar data and techniques may make correlated credit decisions. This raises explainability, fairness and systemic herding risks, so validation and governance remain necessary rather than being reduced.

  1. AModels trained on similar data and methods may produce correlated decisions, and opaque models can embed bias that is hard to detectCorrect
  2. BMachine learning models cannot be used for any consumer lending under current standards
  3. CAlternative data always lowers predictive accuracy compared with bureau data
  4. DAI scoring eliminates the need for any model validation because it self-corrects

Explanation

Opacity, data quality and bias, and common data or model providers creating herding are key concerns. The other options are overstated or false: AI is permitted, alternative data can improve accuracy, and validation remains necessary, arguably more so.

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