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 violations relative to classical actuarial reserving methods, while maintaining comparable capital efficiency. We further discuss calibration and governance considerations required to align model parameters with firm-specific risk appetite and supervisory expectations under Solvency II and Own Risk and Solvency Assessment (ORSA) frameworks.

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