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A logistic regression approach to estimating customer profit loss due to lapses in insurance

Author

Listed:
  • Montserrat Guillén

    (Departament d'Econometria, Estadística i Economia Espanyola. RFA-IREA. University of Barcelona. Spain)

  • Ana María Pérez-Marín

    (Departament d'Econometria, Estadística i Economia Espanyola. RFA-IREA. University of Barcelona. Spain)

  • Montserrat Guillén

    (Departament d'Econometria, Estadística i Economia Espanyola. RFA-IREA. University of Barcelona. Spain)

Abstract

This article focuses on business risk management in the insurance industry. A methodology for estimating the profit loss caused by each customer in the portfolio due to policy cancellation is proposed. Using data from a European insurance company, customer behaviour over time is analyzed in order to estimate the probability of policy cancelation and the resulting potential profit loss due to cancellation. Customers may have up to two different lines of business contracts: motor insurance and other diverse insurance (such as, home contents, life or accident insurance). Implications for understanding customer cancellation behaviour as the core of business risk management are outlined.

Suggested Citation

  • Montserrat Guillén & Ana María Pérez-Marín & Montserrat Guillén, 2011. "A logistic regression approach to estimating customer profit loss due to lapses in insurance," Working Papers XREAP2011-13, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2011.
    • Handle: RePEc:xrp:wpaper:xreap2011-13
    as

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    File URL: http://www.xreap.cat/RePEc/xrp/pdf/XREAP2011-13.pdf
    File Function: Revised version, 2011
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    References listed on IDEAS

    as
    1. Mayers, David & Smith, Clifford W, Jr, 1983. "The Interdependence of Individual Portfolio Decisions and the Demand for Insurance," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 304-311, April.
    2. Babbel, David F, 1985. "The Price Elasticity of Demand for Whole Life Insurance," Journal of Finance, American Finance Association, vol. 40(1), pages 225-239, March.
    3. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.
    4. Patrick L. Brockett & Linda L. Golden & Montserrat Guillen & Jens Perch Nielsen & Jan Parner & Ana Maria Perez‐Marin, 2008. "Survival Analysis of a Household Portfolio of Insurance Policies: How Much Time Do You Have to Stop Total Customer Defection?," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 713-737, September.
    5. J. Dhaene & S. Vanduffel & M. Goovaerts, 2007. "Comonotonicity," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(2), pages 265-278.
    6. Verhoef, P.C. & Donkers, A.C.D., 2001. "Predicting Customer Potential Value: an application in the insurance industry," ERIM Report Series Research in Management ERS-2001-01-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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    Cited by:

    1. 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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