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The customer, the insurer and the market

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  • Christophe Dutang

    () (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

Abstract

Price elasticity studies analyze how customers react to price changes. In this paper, we focus on their effect on the renewal of non-life insurance contracts. Every year insurers face the recurring question of adjusting premiums. Where is the trade off between increasing premium to favour higher projected profit margins and decreasing premiums to obtain a greater market share? Regression models are used to explore the triangular relationship of the customer, the insurer and the market. We conclude that the latter cannot be ignored if we want to get reliable lapse predictions. Furthermore, we also investigate empirical evidence of adverse selection and study its potential impact on lapse decisions.

Suggested Citation

  • Christophe Dutang, 2012. "The customer, the insurer and the market," Post-Print hal-01616152, HAL.
  • Handle: RePEc:hal:journl:hal-01616152
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01616152
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    File URL: https://hal.archives-ouvertes.fr/hal-01616152/document
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    References listed on IDEAS

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    1. Montserrat Guillen & Jan Parner & Chresten Densgsoe & Ana M. Perez-Marin, 2003. "Using Logistic Regression Models to Predict and Understand Why Customers Leave an Insurance Company," World Scientific Book Chapters,in: Intelligent And Other Computational Techniques In Insurance Theory and Applications, chapter 13, pages 465-490 World Scientific Publishing Co. Pte. Ltd..
    2. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521828284.
    3. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    4. Su, Liangjun & White, Halbert, 2014. "Testing conditional independence via empirical likelihood," Journal of Econometrics, Elsevier, vol. 182(1), pages 27-44.
    5. Georges Dionne & Christian Gourieroux & Charles Vanasse, 2001. "Testing for Evidence of Adverse Selection in the Automobile Insurance Market: A Comment," Journal of Political Economy, University of Chicago Press, vol. 109(2), pages 444-473, April.
    6. Pierre-Andre Chiappori & Bernard Salanie, 2000. "Testing for Asymmetric Information in Insurance Markets," Journal of Political Economy, University of Chicago Press, vol. 108(1), pages 56-78, February.
    7. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521535380.
    8. Simon N. Wood, 2008. "Fast stable direct fitting and smoothness selection for generalized additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 495-518, July.
    9. 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.
    10. Dardanoni, V & Li Donni, P, 2008. "Testing For Asymmetric Information In Insurance Markets With Unobservable Types," Health, Econometrics and Data Group (HEDG) Working Papers 08/26, HEDG, c/o Department of Economics, University of York.
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