# Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM

arXiv: https://arxiv.org/abs/2307.07771 · HTML: https://mlsanity.com/paper/2307.07771/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 15 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)
- [Boosting insights in insurance tariff plans with tree-based machine learning methods](https://mlsanity.com/paper/1904.10890/) (2019)
- [Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models](https://mlsanity.com/paper/1508.06378/) (2015)
- [Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Data](https://mlsanity.com/paper/1811.10192/) (2018)
- [Synthetic Dataset Generation of Driver Telematics](https://mlsanity.com/paper/2102.00252/) (2021)
- [Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematics](https://mlsanity.com/paper/2007.03100/) (2020)

## Built on by (most cited first, 5 of 5)

- [Automated Machine Learning in Insurance](https://mlsanity.com/paper/2408.14331/) (2024)
- [Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics](https://mlsanity.com/paper/2406.16206/) (2024)
- [Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes](https://mlsanity.com/paper/2409.00485/) (2024)
- [From Point to probabilistic gradient boosting for claim frequency and severity prediction](https://mlsanity.com/paper/2412.14916/) (2024)
- [Distribution-free inference for LightGBM and GLM with Tweedie loss](https://mlsanity.com/paper/2507.06921/) (2025)

## Abstract (arXiv)

The property and casualty (P&C) insurance industry faces challenges in developing claim predictive models due to the highly right-skewed distribution of positive claims with excess zeros. To address this, actuarial science researchers have employed "zero-inflated" models that combine a traditional count model and a binary model. This paper investigates the use of boosting algorithms to process insurance claim data, including zero-inflated telematics data, to construct claim frequency models. Three popular gradient boosting libraries - XGBoost, LightGBM, and CatBoost - are evaluated and compared to determine the most suitable library for training insurance claim data and fitting actuarial frequency models. Through a comprehensive analysis of two distinct datasets, it is determined that CatBoost is the best for developing auto claim frequency models based on predictive performance. Furthermore, we propose a new zero-inflated Poisson boosted tree model, with variation in the assumption about the relationship between inflation probability p and distribution mean μ, and find that it outperforms others depending on data characteristics. This model enables us to take advantage of particular CatBoost tools, which makes it easier and more convenient to investigate the effects and interactions of various risk features on the frequency model when using telematics data.