Reinforcement Learning Model Enhances Claim Reserving Accuracy in Synthetic Insurance Datasets

Global: Reinforcement Learning Model Enhances Claim Reserving Accuracy in Synthetic Insurance Datasets

A research team has introduced a reinforcement‑learning framework designed to improve the estimation of outstanding claim liabilities (OCL) for general‑insurance portfolios. The study, posted on arXiv in January 2026, outlines a claim‑level Markov decision process (MDP) that updates OCL estimates sequentially throughout a claim’s development period. By treating reserving as a continuous‑action decision problem, the approach seeks to balance predictive accuracy with the stability of reserve revisions.

Markov Decision Process Formulation

The proposed model casts each individual claim as an agent operating within an MDP, where states capture the claim’s development history and actions represent adjustments to liability estimates. A reward function penalizes both large deviations from observed outcomes and abrupt changes in reserve levels, encouraging the algorithm to produce smooth, reliable updates over time.

Learning from All Claim Trajectories

Unlike traditional supervised reserving techniques that rely solely on settled claims, the reinforcement‑learning method can incorporate data from claims that remain open at valuation. This broader learning base mitigates the sample‑size reduction and selection bias that often accompany models trained only on ultimate outcomes.

Practical Enhancements for Actuarial Use

To facilitate deployment in actuarial workflows, the authors add three operational components: (1) an initialization scheme for newly reported claims, (2) a rolling‑settlement procedure that ensures temporal consistency of model parameters, and (3) an importance‑weighting mechanism that addresses portfolio‑level underestimation caused by the rarity of large claims.

Experimental validation employs two synthetic general‑insurance datasets—CAS and SPLICE. The Soft Actor‑Critic implementation demonstrates claim‑level accuracy comparable to existing benchmarks while delivering notably stronger aggregate OCL performance, particularly for immature claim segments that typically drive the bulk of liability uncertainty.

Results suggest that the reinforcement‑learning framework could offer actuaries a more flexible tool for dynamic reserve management, especially in environments where claim development is ongoing and data on settled outcomes are limited. Further research may explore real‑world applications and extensions to other lines of insurance business.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.