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.
Building similarity graph...
Analyzing shared references across papers
Loading...
Satishkumar Rajendran
University of Central Missouri
Building similarity graph...
Analyzing shared references across papers
Loading...
Satishkumar Rajendran (Wed,) studied this question.
www.synapsesocial.com/papers/69a75bb7c6e9836116a23905 — DOI: https://doi.org/10.5281/zenodo.18398224