Skip to main content

An analysis of customer retention and insurance claim patterns using data mining: a case study

Abstract

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.

This is a preview of subscription content, access via your institution.

Author information

Affiliations

Authors

Corresponding author

Correspondence to K A Smith.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Smith, K., Willis, R. & Brooks, M. An analysis of customer retention and insurance claim patterns using data mining: a case study. J Oper Res Soc 51, 532–541 (2000). https://doi.org/10.1057/palgrave.jors.2600941

Download citation

Keywords

  • data mining
  • insurance
  • neural networks
  • classification
  • clustering
  • case study