The Distribution of Automobile Liability Insurance: An Alternative

Hoffer, George E.; Miller, Elbert G.

doi:10.2307/252458

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…Insurance companies classify policy holders into various risk groups based on factors such as territory, demographic variables (such as age, gender and marital status) and other variables (such as use of vehicle, driving record and years of driving experience). These factors are considered predictors of claim costs (Dionne and Vanasse, 1992;Freifelder, 1985;Samson, 1986;Tryfos, 1980;Hoffer and Miller, 1979).…”

Section: Risk Classificationmentioning

confidence: 99%

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

Yeo

Smith

Willis

et al. 2001

Intelligent Systems in Accounting, Finance and Management

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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 datadriven 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.

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