Predictive Modeling in Insurance

Predictive modeling in insurance sits at the intersection of statistics, actuarial judgement, and regulation. Generalized Linear Models (GLMs) remain the workhorse for pricing because they're transparent and easy to defend. Gradient boosting and other machine-learning approaches often win on raw accuracy, but demand more work on interpretability and monitoring.

This article covers a practical workflow: framing the business question, preparing data, choosing a model class, validating out-of-sample, and productionizing with proper monitoring.