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Article

Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models

1
Departament de Matemàtica Econòmica, Financera i Actuarial, Universitat de Barcelona, Diagonal 690, 08034 Barcelona, Spain
2
Department of Statistics, Athens University of Economics and Business, 10434 Athens, Greece
*
Author to whom correspondence should be addressed.
Risks 2020, 8(1), 10; https://doi.org/10.3390/risks8010010
Received: 20 December 2019 / Revised: 20 January 2020 / Accepted: 22 January 2020 / Published: 29 January 2020
(This article belongs to the Special Issue Machine Learning in Insurance)
When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. This approach has interesting features: first, it allows for overdispersion and the zero-inflated model represents a special case, and second, it allows for an elegant interpretation based on the typical clustering application of finite mixture models. k-finite mixture models are applied to a car insurance claim dataset in order to analyse whether the problem of unobserved heterogeneity requires a richer structure for risk classification. Our results show that the data consist of two subpopulations for which the regression structure is different. View Full-Text
Keywords: zero-inflation; overdispersion; automobile insurance; risk classification; risk selection zero-inflation; overdispersion; automobile insurance; risk classification; risk selection
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MDPI and ACS Style

Bermúdez, L.; Karlis, D.; Morillo, I. Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models. Risks 2020, 8, 10. https://doi.org/10.3390/risks8010010

AMA Style

Bermúdez L, Karlis D, Morillo I. Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models. Risks. 2020; 8(1):10. https://doi.org/10.3390/risks8010010

Chicago/Turabian Style

Bermúdez, Lluís, Dimitris Karlis, and Isabel Morillo. 2020. "Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models" Risks 8, no. 1: 10. https://doi.org/10.3390/risks8010010

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