In this paper, we explore advanced modifications to the Tweedie regression model in order to address its limitations in modeling aggregate claims for various types of insurance such as automobile, health, and liability. Traditional Tweedie models, while effective in capturing the probability and magnitude of claims, usually fall short in accurately representing the large incidence of zero claims. Our recommended approach involves a refined modeling of the zero-claim process, together with the integration of boosting methods in order to help leverage an iterative process to enhance predictive accuracy. Despite the inherent slowdown in learning algorithms due to this iteration, several efficient implementation techniques that also help precise tuning of parameters like XGBoost, LightGBM, and CatBoost have emerged. Nonetheless, we chose to utilize CatBoost, an efficient boosting approach that effectively handles categorical and other special types of data. The core contribution of our paper is the assembly of separate modeling for zero claims and the application of tree-based boosting ensemble methods within a CatBoost framework, assuming that the inflated probability of zero is a function of the mean parameter. The efficacy of our enhanced Tweedie model is demonstrated through the application of an insurance telematics dataset, which presents the additional complexity of compositional feature variables. Our modeling results reveal a marked improvement in model performance, showcasing its potential to deliver more accurate predictions suitable for insurance claim analytics.

翻译：本文探讨了对Tweedie回归模型进行高级改进，以解决其在汽车、健康和责任保险等各类保险的聚合索赔建模中的局限性。传统的Tweedie模型虽然在捕捉索赔概率和规模方面有效，但通常难以准确表征大量零索赔的发生情况。我们提出的方法包括对零索赔过程的精细化建模，并结合提升方法以利用迭代过程提高预测准确性。尽管这种迭代会导致学习算法固有的速度下降，但诸如XGBoost、LightGBM和CatBoost等高效实现技术已经出现，这些技术也有助于精确调整参数。尽管如此，我们选择使用CatBoost——一种能有效处理分类及其他特殊数据类型的高效提升方法。本文的核心贡献在于：在假设零值膨胀概率是均值参数的函数的前提下，将零索赔的独立建模与基于树的提升集成方法在CatBoost框架内进行整合。通过应用包含组合特征变量（带来额外复杂性）的保险远程信息处理数据集，我们验证了增强型Tweedie模型的有效性。建模结果显示模型性能显著提升，表明该方法有望为保险索赔分析提供更准确的预测。