Optimal Bonus-Malus Systems using finite mixture models

George Tzougas, Spyridon Vrontos and Nicholas Frangos

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper presents the design of optimal Bonus-Malus Systems using finite mixture models, extending the work of Lemaire (1995; Lemaire, J. (1995) Bonus-Malus Systems in Automobile Insurance. Norwell, MA: Kluwer) and Frangos and Vrontos (2001; Frangos, N. and Vrontos, S. (2001) Design of optimal bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. ASTIN Bulletin, 31(1), 1–22). Specifically, for the frequency component we employ finite Poisson, Delaporte and Negative Binomial mixtures, while for the severity component we employ finite Exponential, Gamma, Weibull and Generalized Beta Type II mixtures, updating the posterior probability. We also consider the case of a finite Negative Binomial mixture and a finite Pareto mixture updating the posterior mean. The generalized Bonus-Malus Systems we propose, integrate risk classification and experience rating by taking into account both the a priori and a posteriori characteristics of each policyholder.

Keywords: BMS; Claim frequency; Claim severity; Mixtures of distributions; A priori classi cation criteria; A posteriori classi cation criteria. (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2014-05
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Citations: View citations in EconPapers (17)

Published in Astin Bulletin, May, 2014, 44(2), pp. 417-444. ISSN: 0515-0361

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