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 parameter like XGBoost, LightGBM, and CatBoost have emerged. Nonetheless, we chose to utilize CatBoost, a 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Tweedie回归模型的高级改进方案，旨在解决其在汽车、健康和责任险等各类保险聚合索赔建模中的局限性。传统的Tweedie模型虽能有效捕捉索赔概率与金额，但通常难以准确表征大量零索赔的分布特征。我们提出的方法通过精细化建模零索赔过程，并结合提升方法以利用迭代过程增强预测精度。尽管迭代过程会自然降低学习算法速度，但XGBoost、LightGBM和CatBoost等高效实现技术已能支持参数的精确调优。我们最终选择采用能有效处理类别特征与特殊数据类型的CatBoost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前提下，构建了零索赔的独立建模框架，并将基于树的提升集成方法应用于CatBoost体系。通过保险车联网数据集的实证分析——该数据集还包含具有组合特征变量的复杂结构——验证了增强型Tweedie模型的有效性。建模结果表明模型性能获得显著提升，证明该方法能够为保险索赔分析提供更精准的预测能力。