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

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  • Ai Cheo Yeo
  • Kate A. Smith
  • Robert J. Willis
  • Malcolm Brooks

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.

Suggested Citation

  • Ai Cheo Yeo & Kate A. Smith & Robert J. Willis & Malcolm Brooks, 2001. "Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(1), pages 39-50, March.
    • Handle: RePEc:wly:isacfm:v:10:y:2001:i:1:p:39-50
    DOI: 10.1002/isaf.196
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    References listed on IDEAS

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    1. K A Smith & R J Willis & M Brooks, 2000. "An analysis of customer retention and insurance claim patterns using data mining: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(5), pages 532-541, May.
    2. von Lanzenauer, Christoph Haehling & Wright, Don D., 1991. "Operational research and insurance," European Journal of Operational Research, Elsevier, vol. 55(1), pages 1-13, November.
    3. Dionne, G & Vanasse, C, 1992. "Automobile Insurance Ratemaking in the Presence of Asymmetrical Information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(2), pages 149-165, April-Jun.
    4. Haehling von Lanzenauer, Christoph & Wright, Don D., 1991. "Operational research and insurance," European Journal of Operational Research, Elsevier, vol. 52(2), pages 129-141, May.
    5. Samson, Danny & Thomas, Howard, 1987. "Linear models as aids in insurance decision making: The estimation of automobile insurance claims," Journal of Business Research, Elsevier, vol. 15(3), pages 247-256, June.
    6. Samson, Danny, 1986. "Designing an automobile insurance classification system," European Journal of Operational Research, Elsevier, vol. 27(2), pages 235-241, October.
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    Cited by:

    1. Joseph Levitas & Konstantin Yavilberg & Oleg Korol & Genadi Man, 2022. "Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction," Papers 2212.09385, arXiv.org, revised Mar 2023.
    2. Caruso, G. & Gattone, S.A. & Fortuna, F. & Di Battista, T., 2021. "Cluster Analysis for mixed data: An application to credit risk evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    3. Daniel E. O'Leary, 2009. "Downloads and citations in Intelligent Systems in Accounting, Finance and Management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 21-31, January.

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