We present a nonparametric model-agnostic framework for building 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 effectiveness of the framework is showcased with simulated and real datasets. When the underlying severity model is a random forest, we extend the two-stage split conformal prediction procedure, showing how the out-of-bag mechanism can be leveraged to eliminate the need for a calibration set and to enable the production of prediction intervals with adaptive width.

翻译：我们提出了一种非参数化的模型无关框架，用于构建具有有限样本统计保证的保险理赔预测区间，并将分裂共形预测技术扩展至两阶段频率-严重度建模领域。该框架的有效性通过模拟数据集和真实数据集得到验证。当底层严重度模型为随机森林时，我们对两阶段分裂共形预测流程进行了扩展，展示了如何利用袋外机制来消除对校准集的需求，并能够生成具有自适应宽度的预测区间。