Conformal prediction of future insurance claims in the regression problem

• URL: http://arxiv.org/abs/2503.03659v2
• Date: Thu, 24 Apr 2025 16:16:24 GMT
• Title: Conformal prediction of future insurance claims in the regression problem
• Authors: Liang Hong,
• Abstract summary: This article introduces conformal prediction -- a general machine learning strategy for valid predictions.<n>The proposed method is both model-free and tuning- parameter-free.<n>It also guarantees finite-sample validity at a pre-assigned coverage probability level.
• Score: 1.7223564681760168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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