Dispersion Modeling in Zero-inflated Tweedie Models with Applications to Insurance Claim Data Analysis

TL;DR

This paper develops a novel approach for dispersion modeling in zero-inflated Tweedie models, enhancing prediction accuracy for insurance claim data with many zeros by integrating gradient boosting and EM algorithms.

Contribution

It introduces a new dispersion modeling technique for zero-inflated Tweedie models, incorporating nonlinear effects and improving prediction performance in insurance data analysis.

Findings

Enhanced prediction accuracy demonstrated through numerical studies

Effective modeling of zero-inflation and dispersion simultaneously

Improved performance over existing methods

Abstract

The Tweedie generalized linear models are commonly applied in the insurance industry to analyze semicontinuous claim data. For better prediction of the aggregated claim size, the mean and dispersion of the Tweedie model are often estimated together using the double generalized linear models. In some actuarial applications, it is common to observe an excessive percentage of zeros, which often results in a decline in the performance of the Tweedie model. The zero-inflated Tweedie model has been recently considered in the literature, which draws inspiration from the zero-inflated Poisson model. In this article, we consider the problem of dispersion modeling of the Tweedie state in the zero-inflated Tweedie model, in addition to the mean modeling. We also model the probability of the zero state based on the generalized expectation-maximization algorithm. To potentially incorporate nonlinear and interaction effects of the covariates, we estimate the mean, dispersion, and zero-state probability using decision-tree-based gradient boosting. We conduct extensive numerical studies to demonstrate the improved performance of our method over existing ones.