Modelling of claim counts using finite mixture models

• Lluís Bermúdez ^[1] ; Dimitris Karlis ^[2]
  1. [1] Universitat de Barcelona

     Universitat de Barcelona

     Barcelona, España

     
  2. [2] Athens University of Economics and Business

     Athens University of Economics and Business

     Dimos Athens, Grecia

     
• Localización: Contributions to risk analysis: risk 2018 / coord. por José María Sarabia Alegría, Faustino Prieto Mendoza, Montserrat Guillén Estany, 2018, ISBN 978-84-9844-683-8, págs. 61-69
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • When modelling insurance claim counts data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach for accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle, and compound frequency models are usually applied to insurance data to account for such characteristics of the data. However, a natural way to deal with the unobserved heterogeneity is to consider mixtures of a simpler model. In this paper, we consider K-finite mixtures of some usual 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. These models are applied to an automobile insurance claims data set in order to analyse the consequences for risk selection or underwriting and risk classification or ratemaking.