Hybrid Tree-based Models for Insurance Claims

TL;DR

This paper introduces a hybrid tree-based modeling approach for insurance claims that improves prediction accuracy over traditional models by combining classification trees and elastic net regression, effectively handling zero-inflated data.

Contribution

The paper presents a novel hybrid two-step tree-based model that enhances prediction accuracy for insurance claims by addressing zero-inflation and allowing targeted hyperparameter tuning.

Findings

Hybrid models outperform traditional Tweedie models in prediction accuracy.

The approach maintains interpretability while improving performance.

Models are effective on both real and synthetic datasets.

Abstract

Two-part models and Tweedie generalized linear models (GLMs) have been used to model loss costs for short-term insurance contract. For most portfolios of insurance claims, there is typically a large proportion of zero claims that leads to imbalances resulting in inferior prediction accuracy of these traditional approaches. This article proposes the use of tree-based models with a hybrid structure that involves a two-step algorithm as an alternative approach to these traditional models. The first step is the construction of a classification tree to build the probability model for frequency. In the second step, we employ elastic net regression models at each terminal node from the classification tree to build the distribution model for severity. This hybrid structure captures the benefits of tuning hyperparameters at each step of the algorithm; this allows for improved prediction accuracy and tuning can be performed to meet specific business objectives. We examine and compare the predictive performance of such a hybrid tree-based structure in relation to the traditional Tweedie model using both real and synthetic datasets. Our empirical results show that these hybrid tree-based models produce more accurate predictions without the loss of intuitive interpretation.