Comparison of regression and tree-based methods for the prediction of zero-inflated claim data

Pricing non-life insurance products is based on the prediction of two components; claim frequency and claim severity. In this study we focus on claim frequency data that has a zero-inflated structure. Although zero-modified regression models such as zero-inflated and hurdle models are used for data sets with excess zeros, machine learning (ML) methods are also preferred for this type of data sets in recent years. When the objective is the prediction, ML methods generally provide more accurate results than regression models especially for large and complex datasets. Tree-based ML methods run decision trees as the base of the algorithm and improve performance by using the predictions of multiple trees. Combining the traditional methods with ML methods is a current popular approach for prediction tasks. Objective of this study is to compare the predictive performance of regression methods and tree-based ML methods for zero-inflated claim frequency data using a real insurance dataset. Motor third party liability insurance claim data from an insurance company in Turkey is used for the case study. To predict claim frequency, generalized linear models (GLM), zero-inflated model and hurdle model are used under Poisson distribution as regression models and regression trees, boosting and GLM-Boost that is a combination of GLM and Boosting algorithm are used as ML methods. Predictive performances of candidate models are compared using both average in-sample and average out-of-sample losses. According to the case study results, ML methods performed better predictive performance than zero-modified models. Specially, GLM-Boost method performed best among others and that is a promising result for the approaches that are combinations of GLM and ML methods.