An analysis of customer retention and insurance claim patterns using data mining: a case study
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The insurance industry is concerned with many problems of interest to the operational research community. This paper presents a case study involving two such problems and solves them using a variety of techniques within the methodology of data mining. The first of these problems is the understanding of customer retention patterns by classifying policy holders as likely to renew or terminate their policies. The second is better understanding claim patterns, and identifying types of policy holders who are more at risk. Each of these problems impacts on the decisions relating to premium pricing, which directly affects profitability. A data mining methodology is used which views the knowledge discovery process within an holistic framework utilising hypothesis testing, statistics, clustering, decision trees, and neural networks at various stages. The impacts of the case study on the insurance company are discussed.
Keywordsdata mining insurance neural networks classification clustering case study
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