Bayesian Bonus-Malus Premium with Poisson-Lindley Distributed Claim Frequency and Lognormal-Gamma DistributedClaim Severity in Automobile Insurance-Reference-Cited by-同舟云学术

Bayesian Bonus-Malus Premium with Poisson-Lindley Distributed Claim Frequency and Lognormal-Gamma DistributedClaim Severity in Automobile Insurance

Published:2020-09-07 Issue: Volume:19 Page:443-451
ISSN:1109-2769
Container-title:WSEAS TRANSACTIONS ON MATHEMATICS
language:en
Short-container-title:

Author:

Moumeesri Adisak¹,Klongdee Watcharin¹,Pongsart Tippatai²

Affiliation:

1. Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, THAILAND

2. Department of Statistics, Faculty of Science, Khon Kaen University, Khon Kaen,THAILAND

Abstract

The traditional automobile insurance bonus-malus system (BMS) merit-rating depends on thenumber of claims. An insured individual who makes a small severity claim is penalized unfairly compared to aninsured person who makes a large severity claim. A model for assigning the bonus-malus premium wasproposed. Consideration was based on both the number and size of the claims that were assumed to follow aPoisson-Lindley distribution and a Lognormal-Gamma distribution, respectively. The Bayesian method wasapplied to compute the bonus-malus premiums, integrated by both frequency and severity components based onthe posterior criteria. Practical examples using a real data set are provided. This approach offers a fairer methodof penalizing all policyholders in the portfolio.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Subject

General Mathematics

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