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Using Logistic Regression Models to Predict and Understand Why Customers Leave an Insurance Company

In: Intelligent And Other Computational Techniques In Insurance Theory and Applications

Author

Listed:
  • Montserrat Guillen

    (Department of d'Econometria, Universitat de Barcelona, Diagonal 690, 08034 Barcelona, Spain)

  • Jan Parner

    (Business Intelligence Unit, Codan Forsikring, Denmark)

  • Chresten Densgsoe

    (Department of d'Econometria, Universitat de Barcelona, Diagonal 690, 08034 Barcelona, Spain)

  • Ana M. Perez-Marin

    (Departamento de Econometría, Estadística y Economía Española, Facultad de Ciencias Económicas y Empresariales de la Universidad de Barcelona, Av. Diagonal, 690, 08034 Barcelona, Spain)

Abstract

The following sections are included:IntroductionQualitative Dependent Variable ModelsModel SpecificationEstimation and InferenceStages in the Modeling ProcessCustomer InformationEmpirical ResultsConclusionsAcknowledgmentsReferences

Suggested Citation

  • 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: A F Shapiro & L C Jain (ed.), Intelligent And Other Computational Techniques In Insurance Theory and Applications, chapter 13, pages 465-490, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812794246_0013
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    Citations

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    Cited by:

    1. 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.
    2. Christophe Dutang, 2012. "The customer, the insurer and the market," Post-Print hal-01616152, HAL.

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