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

TL;DR

This paper introduces a hybrid AI framework combining generative models and reinforcement learning to optimize reinsurance strategies, improving resilience and capital efficiency under complex risk scenarios.

Contribution

It presents a novel integration of VAEs and PPO reinforcement learning for dynamic reinsurance optimization, addressing limitations of traditional static methods.

Findings

Hybrid framework outperforms classical benchmarks in simulated stress tests.

Generative models effectively capture cross-line dependencies.

Reinforcement learning enables adaptive treaty parameter adjustment.

Abstract

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.