Cuestionario vinculado a una guía · Medio Nivel

Prueba de IA en la suscripción de seguros

Explore cómo los modelos de suscripción estiman las reclamaciones esperadas, utilizan datos de conducción y de propiedad, y plantean inquietudes sobre equidad y gobernanza.

Rutas de guía relacionadasIA en la suscripción de seguros
Pregunta 1 de 8

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

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  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