Buhlmann credibility model in predicting claim frequency that follows heterogeneous Weibull count distribution

Tania M. Karina, Siti Nurrohmah, Ida Fithriani

Research output: Contribution to journalConference article

Abstract

In insurance, policyholder's claim experience is usually too limited to be given full credibility in predicting claim frequency, but policyholder's risk is usually a part of a large risk class which collective claim experience can provide information for credible statistical prediction. Credibility could be used to consider both information. By assuming that the policyholder's number of claims, given the policyholder's risk parameter, follows Weibull count distribution over its risk class, this paper explains Buhlmann credibility in predicting claim frequency. Weibull count distribution relaxes the equidispersion assumption of Poisson distribution. Thus, Weibull count distribution can handle non-equidispersed count data. This paper also shows that for a certain type of past claim frequency data, the use of Poisson assumption in Buhlmann credibility model could result in very low credibility factor which underrates the policyholder's experience.

Original languageEnglish
Article number012041
JournalJournal of Physics: Conference Series
Volume1218
Issue number1
DOIs
Publication statusPublished - 31 May 2019
Event3rd International Conference on Mathematics; Pure, Applied and Computation, ICoMPAC 2018 - Surabaya, Indonesia
Duration: 20 Oct 2018 → …

Fingerprint Dive into the research topics of 'Buhlmann credibility model in predicting claim frequency that follows heterogeneous Weibull count distribution'. Together they form a unique fingerprint.

  • Cite this