再保险优化的混合框架：整合生成模型与强化学习
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement Learning
AI总结：
本文提出结合变分自编码器和近端策略优化的混合框架，用于动态调整再保险条约参数，提升资本充足率和抗风险能力，优于传统方法。
摘要：
再保险优化是资本管理和偿付能力的核心，但传统方法依赖于限制性的分布假设和静态计划设计。我们提出一个混合框架，结合变分自编码器学习多线和多年的索赔数据联合分布，以及近端策略优化强化学习动态调整条约参数。该框架明确针对资本和破产概率约束下的预期盈余，连接统计建模与顺序决策制定。通过模拟和压力测试场景，包括疫情型和灾难型冲击，我们证明混合方法比传统比例和停止损失基准更稳健，产生更高的盈余和更低的尾部风险。我们的发现突显了生成模型在捕捉跨线依赖关系方面的有用性，并展示了基于强化学习的动态结构在实际再保险设置中的可行性。贡献包括（i）明确再保险强化学习中的优化目标，（ii）捍卫生成模型相对于参数拟合的实用性，以及（iii）与现有方法的基准测试。本工作展示了混合AI技术如何应对现代挑战，如投资组合多样化、灾难风险和适应性资本分配。
英文摘要：
Reinsurance optimization is a cornerstone of solvency and capital management, yet traditional approaches often rely on restrictive distributional assumptions and static program designs. We propose a hybrid framework that combines Variational Autoencoders (VAEs) to learn joint distributions of multi-line and multi-year claims data with Proximal Policy Optimization (PPO) reinforcement learning to adapt treaty parameters dynamically. The framework explicitly targets expected surplus under capital and ruin-probability constraints, bridging statistical modeling with sequential decision-making. Using simulated and stress-test scenarios, including pandemic-type and catastrophe-type shocks, we show that the hybrid method produces more resilient outcomes than classical proportional and stop-loss benchmarks, delivering higher surpluses and lower tail risk. Our findings highlight the usefulness of generative models for capturing cross-line dependencies and demonstrate the feasibility of RL-based dynamic structuring in practical reinsurance settings. Contributions include (i) clarifying optimization goals in reinsurance RL, (ii) defending generative modeling relative to parametric fits, and (iii) benchmarking against established methods. This work illustrates how hybrid AI techniques can address modern challenges of portfolio diversification, catastrophe risk, and adaptive capital allocation.
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