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 $\mu$, 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.

翻译：财产与意外伤害（P&C）保险行业在开发索赔预测模型时面临挑战，原因在于正索赔数据存在大量零值且呈现高度右偏分布。为解决此问题，精算科学研究人员采用了结合传统计数模型与二元模型的"零膨胀"模型。本文研究了使用提升算法处理保险索赔数据（包括零膨胀的车联网数据）以构建索赔频率模型的方法。我们对三种流行的梯度提升库——XGBoost、LightGBM和CatBoost——进行了评估与比较，以确定最适合训练保险索赔数据及拟合精算频率模型的工具。通过对两个不同数据集的综合分析，确定CatBoost在基于预测性能开发汽车索赔频率模型方面表现最佳。此外，我们提出了一种新的零膨胀泊松提升树模型，该模型对膨胀概率$p$与分布均值$\mu$之间关系的假设进行了变体，并发现其性能根据数据特征优于其他模型。该模型使我们能够利用CatBoost的特定工具，从而在使用车联网数据时，更便捷地研究各种风险特征对频率模型的影响与交互作用。