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The UPA (Underwriting Profitability Analysis) application embodies a new approach to mining Property & Casualty (P&C) insurance claims data for the purpose of constructing predictive models for insurance risks. UPA utilizes the ProbE (Probabilistic Estimation) predictive modeling data mining kernel to discover risk characterization rules by analyzing large and noisy insurance data sets. Each rule defines a distinct risk group and it 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 form an actuarial standpoint. Our approach to modeling insurance risks and the implementation of that approach have been validated in an actual engagement with a P&C firm. The benefit assessment of the results suggest that this methodology provides significant value to the P&C insurance risk management process.
By: C. Apte, E. Grossman, E. Pednault, B. Rosen, F. Tipu, B. White
Published in: IEEE Intelligent Systems and Their Applications, volume 14, (no 6), pages 49-58 in 1999
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