Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry

Abstract

This paper considers the problem of predicting claim costs in the automobile insurance industry. The first stage involves classifying policy holders according to their perceived risk, followed by modelling the claim costs within each risk group. Two methods are compared for the risk classification stage: a data-driven approach based on hierarchical clustering, and a previously published heuristic method that groups policy holders according to pre-defined factors. Regression is used to model the expected claim costs within a risk group. A case study is presented utilizing real data, and both risk classification methods are compared according to a variety of accuracy measures. The results of the case study show the benefits of employing a data-driven approach. © 2001 John Wiley & Sons, Ltd.