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Why is a predictive score not a complete underwriting decision?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. Why is a predictive score not a complete underwriting decision?
    • It does not by itself establish fair use, lawful relevance, explanation, or review
    • A score cannot rank records
    • Every insurer uses identical data
    • Only human decisions can be measured
  2. A claims model reduces payments but appeals rise. What should the insurer investigate?
    • Whether the model’s name changed
    • Whether more images were stored
    • Whether the benchmark score improved
    • Whether wrongful denials or delays increased, not just the payment total
  3. What should be checked before using third-party or credit-related features?
    • Only the feature’s predictive strength
    • Whether the field is numeric
    • Permitted use, provenance, and whether the feature acts as an irrelevant or protected proxy
    • If the vendor provides a dashboard
  4. Why monitor appeals, overrides, and complaints after deployment?
    • They prove the score is accurate
    • They can reveal errors or harms not visible in model metrics
    • They replace group outcome analysis
    • They are unrelated to customer outcomes
  5. What does the NAIC Model Bulletin imply about insurer AI-supported decisions?
    • The bulletin approves all insurer models
    • A model removes the need for governance
    • All state requirements are identical
    • They remain subject to applicable insurance laws, including unfair discrimination rules
  6. Which record helps explain an AI-supported claim outcome?
    • Model version, vendor data, reasons, human review, and corrective action
    • Only the final payment amount
    • The model’s parameter count
    • A copy of the customer’s unrelated policies
  7. Why evaluate across relevant claim conditions and groups?
    • A single average guarantees fairness
    • Errors can vary by claimant or claim context despite acceptable overall accuracy
    • Different conditions cannot affect model error
    • Group checks replace case review
  8. What should a policyholder be able to do where required?
    • Change the model’s training data directly
    • Access other customers’ claims
    • Ask questions about and challenge an outcome through an appropriate path
    • Approve the insurer’s vendor