A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement Learning

ArXi:2501.06404v2 Announce Type: replace-cross 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.