用可验证方法预测保险理赔，确保结果在小样本下依然可靠。
Conformal prediction of future insurance claims in the regression problem
- 采用无模型、无调参的置信预测框架，提升预测可靠性。
- 在预设覆盖概率下保证小样本有效性，避免传统模型偏差。
- 适合监管合规场景，尤其满足欧洲Solvency II资本要求。
当前保险理赔预测多基于统计模型，但易受模型误设、选择偏倚及小样本无效性影响。本文提出一种基于置信预测的方法，同时解决上述三类问题。该方法无需依赖特定模型且无需调参，可在预设覆盖概率水平下保证有限样本下的预测有效性。通过模拟与真实数据案例验证，该方法在保险领域表现优异，尤其适用于满足欧洲保险监管要求——Solvency II中的偿付能力资本要求。
原文摘要 · Abstract (English)
In the current insurance literature, prediction of insurance claims in the regression problem is often performed with a statistical model. This model-based approach may potentially suffer from several drawbacks: (i) model misspecification, (ii) selection effect, and (iii) lack of finite-sample validity. This article addresses these three issues simultaneously by employing conformal prediction -- a general machine learning strategy for valid predictions. The proposed method is both model-free and tuning-parameter-free. It also guarantees finite-sample validity at a pre-assigned coverage probability level. Examples, based on both simulated and real data, are provided to demonstrate the excellent performance of the proposed method and its applications in insurance, especially regarding meeting the solvency capital requirement of European insurance regulation, Solvency II.
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