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IAI Actuarial Core Principles · Business Management · Issues and challenges in each main practice area

A Mumbai-based life insurer asks its actuarial team to build a machine-learning model that uses customers' smartphone activity data to price term policies. The team finds the model is accurate but cannot easily explain why individual applicants receive higher premiums. Which challenge is most directly raised by this situation?

The main challenge is model transparency and fairness. When a machine-learning model prices policies using new data but cannot be explained, the actuary must still justify decisions and guard against unfair discrimination, so explainability and ethical use of data become central professional concerns.

  1. AModel transparency and fairness, because decisions affecting customers must be explainable and not unfairly discriminatoryCorrect
  2. BReserve inadequacy, because accurate models always understate liabilities
  3. CLoss of separate legal personality of the insurer
  4. DA breach of the rule that premiums must be identical for all applicants
  5. A requirement that only Fellows may use statistical software

Explanation

Opaque models that use new data sources raise explainability, bias and fairness issues. Actuaries must be able to justify and communicate the basis of pricing. The other options misstate legal or technical points: premiums are legitimately risk-differentiated, and accuracy does not imply understated reserves.

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