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A mathematical programming approach to optimise insurance premium pricing within a data mining framework

Author

Listed:
  • A C Yeo

    (Monash University)

  • K A Smith

    (Monash University)

  • R J Willis

    (Monash University)

  • M Brooks

    (Australian Associated Motor Insurers Limited)

Abstract

In this paper we provide evidence of the benefits of an approach which combines data mining and mathematical programming to determining the premium to charge automobile insurance policy holders in order to arrive at an optimal portfolio. An non-linear integer programming formulation is proposed to determine optimal premiums based on the insurer's need to find a balance between profitability and market share. The non-linear integer programming approach to solving this problem is used within a data mining framework which consists of three components: classifying policy holders into homogenous risk groups and predicting the claim cost of each group using k-means clustering; determining the price sensitivity (propensity to pay) of each group using neural networks; and combining the results of the first two components to determine the optimal premium to charge. We have earlier presented the results of the first two components. In this paper we present the results of the third component. Using our approach, we have been able to increase revenue without affecting termination rates and market share.

Suggested Citation

  • A C Yeo & K A Smith & R J Willis & M Brooks, 2002. "A mathematical programming approach to optimise insurance premium pricing within a data mining framework," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1197-1203, November.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:11:d:10.1057_palgrave.jors.2601413
    DOI: 10.1057/palgrave.jors.2601413
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    Citations

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    Cited by:

    1. Şerafettin SEVİM & Birol YILDIZ & Nilüfer DALKILIÇ, 2016. "Risk Assessment for Accounting Professional Liability Insurance," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).
    2. Ja-Shen Chen & Russell K H Ching & Yi-Shen Lin, 2004. "An extended study of the K-means algorithm for data clustering and its applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(9), pages 976-987, September.
    3. Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
    4. X Cai & K L Teo & X Q Yang & X Y Zhou, 2004. "Minimax portfolio optimization: empirical numerical study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 65-72, January.
    5. Yann Braouezec, 2015. "Public versus Private Insurance System with (and without) Transaction Costs: Optimal Segmentation Policy of an Informed monopolistPublic versus Private Insurance System with (and without) Transaction ," Working Papers 2013-ECO-23, IESEG School of Management, revised May 2014.

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