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AI în chestionarul de asigurare

Explorați modul în care modelele de subscriere estimează daunele așteptate, utilizează datele despre șofer și proprietăți și ridică preocupări privind echitatea și guvernanța.

Căi de ghidare aferenteAI în subscrierea de asigurări
Întrebare 1 de 8

In a simplified hypothetical risk model, how is expected loss for one exposure calculated?

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Răspunsurile și explicațiile apar pe măsură ce susțineți testul.

  1. In a simplified hypothetical risk model, how is expected loss for one exposure calculated?
    • Claim probability multiplied by claim severity
    • Claim probability plus the policyholder’s ZIP code
    • Severity divided by the number of features
    • Premium multiplied by the number of claims
  2. Why might a regulated insurer document model inputs and validation?
    • To guarantee that a model never produces biased outcomes
    • To support governance, accountability and review of AI systems
    • To avoid retaining any information about the model
    • To replace actuarial review in every jurisdiction
  3. Under the NAIC model bulletin, who remains responsible for AI-supported insurance decisions complying with applicable law?
    • The software vendor alone
    • The applicant whose data were analyzed
    • The insurer using the AI system
    • The model itself as an independent legal person
  4. What fairness risk should be tested when a model uses variables correlated with protected traits?
    • The model becomes unable to estimate any claims
    • Every applicant receives the same premium
    • The model automatically removes all protected-class effects
    • A proxy variable may contribute to disparate outcomes
  5. In an underwriting model, what is a feature?
    • An input variable used by the model to estimate a risk-related outcome
    • A legal approval for a premium
    • A final insurance claim payment
    • A guarantee that a prediction is fair
  6. What does SHAP provide for a model prediction?
    • A legal approval for the resulting premium
    • Feature-attribution values that explain contributions under the SHAP framework
    • Proof that the model is unbiased
    • A guarantee that a feature caused the outcome
  7. Which driving signals does Progressive say its Snapshot program may measure?
    • Medical history, prescriptions, and blood pressure
    • Social-media sentiment and shopping history
    • Time of day, hard braking or acceleration, and amount driven
    • Vehicle paint color, route scenery, and music choice
  8. How can aerial imagery support property underwriting in Zurich's Nearmap example?
    • It directly measures a home's indoor air quality
    • It replaces all policyholder disclosures and inspections
    • It predicts the exact date a roof will fail
    • It can provide imagery and roof-condition or maintenance indicators for risk assessment