KAFKOVÁ, Silvie. Bonus-Malus Systems in Vehicle Insurance. In Iacob, AI. Procedia Finance and Economics. Amsterdam: Elsevier B.V., 2015. p. 216-222, 7 pp. ISSN 2212-5671. doi:10.1016/S2212-5671(15)00354-8.
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Basic information
Original name Bonus-Malus Systems in Vehicle Insurance
Authors KAFKOVÁ, Silvie (203 Czech Republic, guarantor, belonging to the institution).
Edition Amsterdam, Procedia Finance and Economics, p. 216-222, 7 pp. 2015.
Publisher Elsevier B.V.
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50600 5.6 Political science
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
RIV identification code RIV/00216224:14560/15:00082157
Organization unit Faculty of Economics and Administration
ISSN 2212-5671
Doi http://dx.doi.org/10.1016/S2212-5671(15)00354-8
UT WoS 000360103600032
Keywords in English annual claim frequency; generalized linear model; bonus.-malus system; analysis of deviance
Tags International impact, Reviewed
Changed by Changed by: Mgr. Silvie Zlatošová, Ph.D., učo 175424. Changed: 17/9/2015 12:37.
Abstract
Actuaries in insurance companies try to design a tariff structure that will fairly distribute the burden of claims among policyholders. Therefore they try to find the best model for an estimation of the insurance premium. The paper deals with an estimate of a priori annual claim frequency and application of bonus-malus system in the vehicle insurance. In this paper, analysis of the portfolio of vehicle insurance data using generalized linear model (GLM) is performed. Based on large real-world sample of data from 67 857 vehicles, the present study proposes a classification analysis approach addressing the selection of predictor variables. The models with different predictor variables are compared by the analysis of deviance. Based on this comparison, the model for the best estimate of annual claim frequency is chosen. Then the bonus-malus (BM) system is used for each class of drivers and Bayesian relative premium is calculated. Finally a fairer premium for different groups of drivers is proposed.
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