[2504.09396] Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes

Computer Science > Machine Learning arXiv:2504.09396 (cs) [Submitted on 13 Apr 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes Authors:Stella C. Dong View a PDF of the paper titled Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes, by Stella C. Dong View PDF HTML (experimental) Abstract:We develop a reinforcement learning (RL) framework for insurance loss reserving that formulates reserve setting as a finite-horizon sequential decision problem under claim development uncertainty, macroeconomic stress, and solvency governance. The reserving process is modeled as a Markov Decision Process (MDP) in which reserve adjustments influence future reserve adequacy, capital efficiency, and solvency outcomes. A Proximal Policy Optimization (PPO) agent is trained using a risk-sensitive reward that penalizes reserve shortfall, capital inefficiency, and breaches of a volatility-adjusted solvency floor, with tail risk explicitly controlled through Conditional Value-at-Risk (CVaR). To reflect regulatory stress-testing practice, the agent is trained under a regime-aware curriculum and evaluated using both regime-stratified simulations and fixed-shock stress scenarios. Empirical results for Workers Compensation and Other Liability illustrate how the proposed RL-CVaR policy improves tail-risk control and reduces solvency violat...