Hybrid Generative–Predictive Models for Customer Risk Profiling in Insurance

Hybrid generative–predictive models are increasingly relevant for customer risk profiling in insurance because they connect two complementary capabilities: generative learning for creating realistic, privacy-aware synthetic tabular records and predictive learning for estimating individual risk probabilities used in underwriting, pricing, and claims decisioning. This paper reviews the research landscape and proposes a methodology where a tabular generative module supports a calibrated risk predictor through controlled augmentation, stress testing under portfolio shift, and privacy-risk evaluation. An illustrative experimental template is provided to show how discrimination, calibration, and privacy–utility trade-offs can be reported for insurance risk tasks. The paper concludes by outlining practical future directions for reliable deployment, including governance, distribution shift monitoring, uncertainty quantification, and fairness-aware evaluation.