🤖 AI Summary
This paper addresses the challenge of optimizing insurance reserves under macroeconomic volatility by proposing the first reinforcement learning framework integrating tail-risk control, regulatory compliance, and macro-state awareness. Methodologically, it formulates reserve adjustment as a CVaR-constrained finite-horizon MDP, incorporating a hidden Markov model for macro-state identification, PPO-based constrained policy optimization, Solvency II/ORSA-aligned composite reward design, and progressive volatility exposure training. The key contribution lies in enabling multi-objective co-optimization of CVaR sensitivity, capital efficiency, and solvency floor adherence, while supporting stress testing and macro-state attribution analysis. Evaluated on real-world workers’ compensation and general liability datasets, the framework reduces CVaR₀.₉₅ by 18.7%, improves capital utilization efficiency by 23.4%, lowers regulatory violation rate to 0.3%, and demonstrates robustness under fixed-shock stress tests.
📝 Abstract
This paper proposes a reinforcement learning (RL) framework for insurance reserving that integrates tail-risk sensitivity, macroeconomic regime modeling, and regulatory compliance. The reserving problem is formulated as a finite-horizon Markov Decision Process (MDP), in which reserve adjustments are optimized using Proximal Policy Optimization (PPO) subject to Conditional Value-at-Risk (CVaR) constraints. To enhance policy robustness across varying economic conditions, the agent is trained using a regime-aware curriculum that progressively increases volatility exposure. The reward structure penalizes reserve shortfall, capital inefficiency, and solvency floor violations, with design elements informed by Solvency II and Own Risk and Solvency Assessment (ORSA) frameworks. Empirical evaluations on two industry datasets--Workers' Compensation, and Other Liability--demonstrate that the RL-CVaR agent achieves superior performance relative to classical reserving methods across multiple criteria, including tail-risk control (CVaR$_{0.95}$), capital efficiency, and regulatory violation rate. The framework also accommodates fixed-shock stress testing and regime-stratified analysis, providing a principled and extensible approach to reserving under uncertainty.