Motor Insurance Accidental Damage Claims Modeling with Factor Collapsing and Bayesian Model Averaging

TL;DR

This paper introduces a novel approach combining factor collapsing and Bayesian model averaging to improve motor insurance accidental damage claims modeling, addressing model uncertainty and categorical variable complexity.

Contribution

It proposes a new method for optimal factor collapsing combined with Bayesian model averaging to enhance model accuracy and interpretability in insurance risk modeling.

Findings

Improved model parsimony and interpretability.

Effective handling of categorical variable levels.

Enhanced risk prediction accuracy.

Abstract

Accidental damage is a typical component of motor insurance claim. Modeling of this nature generally involves analysis of past claim history and different characteristics of the insured objects and the policyholders. Generalized linear models (GLMs) have become the industry's standard approach for pricing and modeling risks of this nature. However, the GLM approach utilizes a single "best" model on which loss predictions are based, which ignores the uncertainty among the competing models and variable selection. An additional characteristic of motor insurance data sets is the presence of many categorical variables, within which the number of levels is high. In particular, not all levels of such variables may be statistically significant and rather some subsets of the levels may be merged to give a smaller overall number of levels for improved model parsimony and interpretability. A method is proposed for assessing the optimal manner in which to collapse a factor with many levels into one with a smaller number of levels, then Bayesian model averaging (BMA) is used to blend model predictions from all reasonable models to account for factor collapsing uncertainty. This method will be computationally intensive due to the number of factors being collapsed as well as the possibly large number of levels within factors. Hence a stochastic optimisation is proposed to quickly find the best collapsing cases across the model space.