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Association Rules for Understanding Policyholder Lapses

Author

Listed:
  • Himchan Jeong

    (Department of Mathematics, University of Connecticut, Storrs, CT 06269-1009, USA)

  • Guojun Gan

    (Department of Mathematics, University of Connecticut, Storrs, CT 06269-1009, USA)

  • Emiliano A. Valdez

    (Department of Mathematics, University of Connecticut, Storrs, CT 06269-1009, USA)

Abstract

For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used nine-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.

Suggested Citation

  • Himchan Jeong & Guojun Gan & Emiliano A. Valdez, 2018. "Association Rules for Understanding Policyholder Lapses," Risks, MDPI, vol. 6(3), pages 1-18, July.
  • Handle: RePEc:gam:jrisks:v:6:y:2018:i:3:p:69-:d:156870
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    References listed on IDEAS

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    1. Frees, Edward W. & Shi, Peng & Valdez, Emiliano A., 2009. "Actuarial Applications of a Hierarchical Insurance Claims Model," ASTIN Bulletin, Cambridge University Press, vol. 39(1), pages 165-197, May.
    2. Guelman, Leo & Guillén, Montserrat & Pérez-Marín, Ana M., 2014. "A survey of personalized treatment models for pricing strategies in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 58(C), pages 68-76.
    3. Frees, Edward W. & Valdez, Emiliano A., 2008. "Hierarchical Insurance Claims Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1457-1469.
    4. Christophe Dutang, 2012. "The customer, the insurer and the market," Post-Print hal-01616152, HAL.
    5. Catalina Bolancé & Montserrat Guillen & Jens Perch Nielsen & Fredrik Thuring, 2018. "Price and Profit Optimization for Financial Services," Risks, MDPI, vol. 6(1), pages 1-12, February.
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    Cited by:

    1. Jeong, Himchan & Valdez, Emiliano A., 2020. "Predictive compound risk models with dependence," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 182-195.
    2. Manuel Leiria & Nelson Matos & Efigénio Rebelo, 2021. "Non-life insurance cancellation: a systematic quantitative literature review," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 46(4), pages 593-613, October.
    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.

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