We present a model-agnostic framework for the construction of prediction intervals of insurance claims, with finite sample statistical guarantees, extending the technique of split conformal prediction to the domain of two-stage frequency-severity modeling. The framework effectiveness is showcased with simulated and real datasets using classical parametric models and contemporary machine learning methods. When the underlying severity model is a random forest, we extend the two-stage split conformal prediction algorithm, showing how the out-of-bag mechanism can be leveraged to eliminate the need for a calibration set in the conformal procedure.

翻译：本文提出了一种与模型无关的框架，用于构建具有有限样本统计保证的保险索赔预测区间，将分割保形预测技术扩展至两阶段频度-严重度建模领域。通过使用经典参数模型和现代机器学习方法在模拟与真实数据集上的实验，验证了该框架的有效性。当基础严重度模型为随机森林时，我们扩展了两阶段分割保形预测算法，展示了如何利用袋外机制消除保形过程中对校准集的需求。