Artikel

Modelling unobserved heterogeneity in claim counts using finite mixture models

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.

Language
Englisch

Bibliographic citation
Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 1 ; Pages: 1-13 ; Basel: MDPI

Classification
Wirtschaft
Subject
automobile insurance
overdispersion
risk classification
risk selection
zero-inflation

Event
Geistige Schöpfung
(who)
Bermúdez, Lluís
Karlis, Dimitris
Morillo, Isabel
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2020

DOI
doi:10.3390/risks8010010
Handle
Last update
20.09.2024, 8:24 AM CEST

Data provider

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Object type

  • Artikel

Associated

  • Bermúdez, Lluís
  • Karlis, Dimitris
  • Morillo, Isabel
  • MDPI

Time of origin

  • 2020

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