Bivariate Gamma Mixture of Experts Models for Joint Insurance Claims Modeling

TL;DR

This paper introduces a novel bivariate gamma mixture of experts model for joint insurance claims modeling, capturing dependencies between risks and improving prediction accuracy by incorporating covariates and clustering.

Contribution

It develops a new family of mixture of experts models with bivariate gamma distributions that directly model dependence and include covariates in both gating and expert networks.

Findings

Model improves claim prediction accuracy on real data

Clustering reveals subgroups with strong dependence

Parsimonious parameterizations enhance model interpretability

Abstract

In general insurance, risks from different categories are often modeled independently and their sum is regarded as the total risk the insurer takes on in exchange for a premium. The dependence from multiple risks is generally neglected even when correlation could exist, for example a single car accident may result in claims from multiple risk categories. It is desirable to take the covariance of different categories into consideration in modeling in order to better predict future claims and hence allow greater accuracy in ratemaking. In this work multivariate severity models are investigated using mixture of experts models with bivariate gamma distributions, where the dependence structure is modeled directly using a GLM framework, and covariates can be placed in both gating and expert networks. Furthermore, parsimonious parameterisations are considered, which leads to a family of bivariate gamma mixture of experts models. It can be viewed as a model-based clustering approach that clusters policyholders into sub-groups with different dependencies, and the parameters of the mixture models are dependent on the covariates. Clustering is shown to be important in separating the data into sub-groupings where strong dependence is often present, even if the overall data set exhibits only weak dependence. In doing so, the correlation within different components features prominently in the model. It is shown that, by applying to both simulated data and a real-world Irish GI insurer data set, claim predictions can be improved.