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An Integrated Data Mining Approach to Premium Pricing for the Automobile Insurance Industry

In: Intelligent And Other Computational Techniques In Insurance Theory and Applications

Author

Listed:
  • A. C. Yeo

    (Monash University School of Business Systems, PO Box 63B, Victoria 3800, Australia)

  • K. A. Smith

    (School of Business Systems, Faculty of Information Technology, PO Box 63B, Monash University, Victoria 3800, Australia)

Abstract

The following sections are included:IntroductionA Data Mining ApproachRisk Classification and Prediction of Claim CostRisk ClassificationK-Means Clustering ModelFuzzy CMeans Clustering ModelHeuristic ModelPrediction of Claim CostK-Means Clustering ModelFuzzy C-Means Clustering ModelHeuristic ModelResultsPrediction of Retention Rates and Price SensitivityPrediction of Retention RateNeural Network ModelDetermining Decision ThresholdsAnalyzing Prediction AccuracyGenerating More Homogeneous ModelsCombining Small ClustersResultsPrice Sensitivity AnalysisResultsDetermining an Optimal Portfolio of Policy HoldersResultsConclusionsReferences

Suggested Citation

  • A. C. Yeo & K. A. Smith, 2003. "An Integrated Data Mining Approach to Premium Pricing for the Automobile Insurance Industry," World Scientific Book Chapters, in: A F Shapiro & L C Jain (ed.), Intelligent And Other Computational Techniques In Insurance Theory and Applications, chapter 5, pages 199-227, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812794246_0005
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