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Generalized Linear Models in Vehicle Insurance

Author

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  • Silvie Kafková

    (Masaryk University, Faculty of Economics and Administration, Lipová 41a, 602 00 Brno, Czech Republic)

  • Lenka Křivánková

    (Masaryk University, Faculty of Science, Kotlářská 2, 611 37 Brno, Czech Republic)

Abstract

Actuaries in insurance companies try to find the best model for an estimation of insurance premium. It depends on many risk factors, e.g. the car characteristics and the profile of the driver. In this paper, an analysis of the portfolio of vehicle insurance data using a generalized linear model (GLM) is performed. The main advantage of the approach presented in this article is that the GLMs are not limited by inflexible preconditions. Our aim is to predict the relation of annual claim frequency on given risk factors. Based on a large real-world sample of data from 57 410 vehicles, the present study proposed a classification analysis approach that addresses the selection of predictor variables. The models with different predictor variables are compared by analysis of deviance and Akaike information criterion (AIC). Based on this comparison, the model for the best estimate of annual claim frequency is chosen. All statistical calculations are computed in R environment, which contains stats package with the function for the estimation of parameters of GLM and the function for analysis of deviation.

Suggested Citation

  • Silvie Kafková & Lenka Křivánková, 2014. "Generalized Linear Models in Vehicle Insurance," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 62(2), pages 383-388.
    • Handle: RePEc:mup:actaun:actaun_2014062020383
    DOI: 10.11118/actaun201462020383
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    References listed on IDEAS

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    1. Antonio, Katrien & Beirlant, Jan, 2007. "Actuarial statistics with generalized linear mixed models," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 58-76, January.
    2. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, January.
    3. Gschlossl, Susanne & Schoenmaekers, Pascal & Denuit, Michel, 2011. "Risk classification in life insurance: Methodology and case study," LIDAM Reprints ISBA 2011021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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    1. Azaare Jacob & Zhao Wu, 2020. "An Alternative Pricing System through Bayesian Estimates and Method of Moments in a Bonus-Malus Framework for the Ghanaian Auto Insurance Market," JRFM, MDPI, vol. 13(7), pages 1-15, July.

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