ORIGINAL COMPANY-CONTEXT PRACTICE
Evaluate predictions with immature insurance outcomes
Original practice scenario: A hypothetical insurance analyst trains a model on claims that take months to resolve. How would you prevent recent claims from appearing artificially inexpensive?
Written for independent practice using company context; this is not a verified question from a company interview.
A REASONED APPROACH
Model answer.
Treat outcome maturity as part of the observation design. A claim with no recorded payment yet is not necessarily a zero-cost claim, and later records may revise earlier estimates.
Work through it
- Define prediction time, claim cohort and target maturity, retaining the date each payment or adjustment became available to the analyst.
- Use time-separated evaluation with equally mature outcome windows, comparing against a transparent cohort-based baseline before more complex predictors.
- Report performance by claim age and relevant segments, plus sensitivity to unresolved outcomes and the proportion excluded from mature evaluation.
Tradeoffs to discuss
- Waiting for mature outcomes improves comparability but delays feedback; preliminary measures are faster while requiring explicit uncertainty and later reconciliation.
Common pitfalls
- Do not train on final settlement fields that were unavailable when the prediction would have been made.
- Separate claim frequency from claim amount so a changing case mix does not masquerade as better prediction.