Maswali yanayounganishwa na mwongozo · Kati Kiwango

AI katika Maswali ya Uandishi wa Bima

Chunguza jinsi miundo ya uandishi wa chini inavyokadiria madai yanayotarajiwa, kutumia data ya kuendesha gari na mali, na kuibua masuala ya haki na utawala.

Njia za mwongozo zinazohusianaAI Katika Uandishi wa Bima
Swali 1 ya 8

Katika modeli iliyorahisishwa ya hatari dhahania, hasara inayotarajiwa kwa mfiduo mmoja huhesabiwaje?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. Katika modeli iliyorahisishwa ya hatari dhahania, hasara inayotarajiwa kwa mfiduo mmoja huhesabiwaje?
    • 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. Kwa nini inaweza pembejeo na uthibitisho wa hati iliyodhibitiwa ya hati ya bima?
    • 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. Chini ya taarifa ya muundo wa NAIC, ni nani atasalia kuwajibika kwa maamuzi ya bima inayoungwa mkono na AI yanayotii sheria zinazotumika?
    • 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. Ni hatari gani ya haki inapaswa kujaribiwa wakati mtindo unatumia vigeu vinavyohusiana na sifa zinazolindwa?
    • 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. Katika modeli ya uandishi, kipengele ni nini?
    • 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