Probabilistic estimation-based data mining for discovering insurance risks

Probabilistic estimation-based data mining for discovering insurance risks

IBM's underwriting profitability analysis application mines property and casualty insurance policy and claims data to construct predictive models for insurance risks. UPA uses the ProbE data-mining kernel to discover risk-characterization rules by analyzing large, noisy data sets.

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Bibliographische Detailangaben
Veröffentlicht in:IEEE intelligent systems & their applications 1999-11, Vol.14 (6), p.49-58
Hauptverfasser: Apte, C., Grossman, E., Pednault, E.P.D., Rosen, B.K., Tipu, F.A., White, B.
Format: Artikel
Sprache:eng
Schlagworte:
Casualties
Companies
Data mining
Electrical capacitance tomography
Insurance
Kernel
Kernels
Predictive models
Probability theory
Probes
Profitability
Risk
Risk analysis
Sensitivity analysis
Underwriting
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Beschreibung
Zusammenfassung:IBM's underwriting profitability analysis application mines property and casualty insurance policy and claims data to construct predictive models for insurance risks. UPA uses the ProbE data-mining kernel to discover risk-characterization rules by analyzing large, noisy data sets.
ISSN:1094-7167
DOI:10.1109/5254.809568