Insurance Risk Modeling Using Data Mining Technology

The UPA (Underwriting Profitability Analysis) application embodies a new approach to mining Property & Casualty (P&C) insurance policy and claims data for the purpose of constructing predictive models for insurance risks. UPA utilizes the ProbE (Probabilistic Estimation) predictive modeling class library to discover risk characterization rules by analyzing large and noisy insurance data sets. Each rule defines a distinct risk group and its level of risk. To satisfy regulatory constraints, the risk groups are mutually exclusive and exhaustive. The rules generated by ProbE are statistically rigorous, interpretable, and credible from an actuarial standpoint. The ProbE library itself is scalable, extensible, and embeddable. Our approach to modeling insurance risks and the implementation of that approach have been validated in an actual engagement with a P&C insurance firm. The benefit assessment of the results suggest that this methodology provides significant value to the P&C insurance risk management process.