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Modeling the Frequency of Auto Insurance Claims by Means of Poisson and Negative Binomial Models

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  • David Mihaela
  • Jemna Dănuţ-Vasile

    (Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iaşi, Romania)

Abstract

Within non-life insurance pricing, an accurate evaluation of claim frequency, also known in theory as count data, represents an essential part in determining an insurance premium according to the policyholder’s degree of risk. Count regression analysis allows the identification of the risk factors and the prediction of the expected frequency of claims given the characteristics of policyholders. The aim of this paper is to verify several hypothesis related to the methodology of count data models and also to the risk factors used to explain the frequency of claims. In addition to the standard Poisson regression, Negative Binomial models are applied to a French auto insurance portfolio. The best model was chosen by means of the log-likelihood ratio and the information criteria. Based on this model, the profile of the policyholders with the highest degree of risk is determined

Suggested Citation

  • David Mihaela & Jemna Dănuţ-Vasile, 2015. "Modeling the Frequency of Auto Insurance Claims by Means of Poisson and Negative Binomial Models," Scientific Annals of Economics and Business, Sciendo, vol. 62(2), pages 151-168, July.
    • Handle: RePEc:vrs:aicuec:v:62:y:2015:i:2:p:151-168:n:2
    DOI: 10.1515/aicue-2015-0011
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    References listed on IDEAS

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    Cited by:

    1. Simon CK Lee, 2020. "Delta Boosting Implementation of Negative Binomial Regression in Actuarial Pricing," Risks, MDPI, vol. 8(1), pages 1-21, February.
    2. Shengkun Xie & Anna T. Lawniczak, 2018. "Estimating Major Risk Factor Relativities in Rate Filings Using Generalized Linear Models," IJFS, MDPI, vol. 6(4), pages 1-14, October.
    3. Shengkun Xie & Rebecca Luo, 2022. "Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions," Mathematics, MDPI, vol. 10(10), pages 1-19, May.
    4. Zhengmin Duan & Yonglian Chang & Qi Wang & Tianyao Chen & Qing Zhao, 2018. "A Logistic Regression Based Auto Insurance Rate-Making Model Designed for the Insurance Rate Reform," IJFS, MDPI, vol. 6(1), pages 1-16, February.

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