IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v6y2018i3p69-d156870.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/6/3/69/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/6/3/69/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peng Shi & Wei Zhang, 2011. "A copula regression model for estimating firm efficiency in the insurance industry," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2271-2287.
    2. Jeong, Himchan & Valdez, Emiliano A., 2020. "Predictive compound risk models with dependence," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 182-195.
    3. Yang, Xipei & Frees, Edward W. & Zhang, Zhengjun, 2011. "A generalized beta copula with applications in modeling multivariate long-tailed data," Insurance: Mathematics and Economics, Elsevier, vol. 49(2), pages 265-284, September.
    4. Marie Michaelides & Mathieu Pigeon & H'el`ene Cossette, 2022. "Individual Claims Reserving using Activation Patterns," Papers 2208.08430, arXiv.org, revised Aug 2023.
    5. Pechon, Florian & Denuit, Michel & Trufin, Julien, 2019. "Home and Motor insurance joined at a household level using multivariate credibility," LIDAM Discussion Papers ISBA 2019013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Araichi, Sawssen & Peretti, Christian de & Belkacem, Lotfi, 2017. "Reserve modelling and the aggregation of risks using time varying copula models," Economic Modelling, Elsevier, vol. 67(C), pages 149-158.
    7. Shi, Peng & Valdez, Emiliano A., 2014. "Multivariate negative binomial models for insurance claim counts," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 18-29.
    8. Chenglong Ye & Lin Zhang & Mingxuan Han & Yanjia Yu & Bingxin Zhao & Yuhong Yang, 2022. "Combining Predictions of Auto Insurance Claims," Econometrics, MDPI, vol. 10(2), pages 1-15, April.
    9. Catalina Bolancé & Raluca Vernic, 2017. "“Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution”," IREA Working Papers 201718, University of Barcelona, Research Institute of Applied Economics, revised Oct 2017.
    10. Erengul Dodd & George Streftaris, 2017. "Prediction of settlement delay in critical illness insurance claims by using the generalized beta of the second kind distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 273-294, February.
    11. Evgenii V. Gilenko & Elena A. Mironova, 2017. "Modern claim frequency and claim severity models: An application to the Russian motor own damage insurance market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1311097-131, January.
    12. Mihaela Covrig & Iulian Mircea & Gheorghita Zbaganu & Alexandru Coser & Alexandru Tindeche, 2015. "Using R In Generalized Linear Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 33-45, September.
    13. Bermúdez i Morata, Lluís, 2009. "A priori ratemaking using bivariate Poisson regression models," Insurance: Mathematics and Economics, Elsevier, vol. 44(1), pages 135-141, February.
    14. Lluis Bermúdez i Morata, 2008. "A priori ratemaking using bivariate poisson regression models," Working Papers XREAP2008-09, Xarxa de Referència en Economia Aplicada (XREAP), revised Jul 2008.
    15. Jean Pinquet, 2012. "Experience rating in non-life insurance," Working Papers hal-00677100, HAL.
    16. Tsyganov, Aleksander & Baskakov, Valery & Yazykov, Andrey & Sheparnev, Nikolay & Yanenko, Evgeny & Grysenkova, Yulia, 2019. "The impact of the bonus-malus system on the insurance ratemaking in the system of compulsory insurance of the responsibility of transport owners in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 56, pages 123-141.
    17. Andreas Bayerstadler & Franz Benstetter & Christian Heumann & Fabian Winter, 2014. "A predictive modeling approach to increasing the economic effectiveness of disease management programs," Health Care Management Science, Springer, vol. 17(3), pages 284-301, September.
    18. Shi, Peng & Zhao, Zifeng, 2024. "Enhanced pricing and management of bundled insurance risks with dependence-aware prediction using pair copula construction," Journal of Econometrics, Elsevier, vol. 240(1).
    19. Crevecoeur, Jonas & Robben, Jens & Antonio, Katrien, 2022. "A hierarchical reserving model for reported non-life insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 158-184.
    20. Zhou, Rui & Ji, Min, 2021. "Modelling mortality dependence: An application of dynamic vine copula," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 241-255.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:6:y:2018:i:3:p:69-:d:156870. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.