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

TL;DR

This work tackles reinsurance optimization under dynamic, high-dimensional claim distributions. It proposes a hybrid framework that combines Variational Autoencoders (VAEs) for generating diverse claim scenarios with Proximal Policy Optimization (PPO) for sequentially adjusting reinsurance parameters, enabling adaptive decisions over a discrete-time horizon. Comprehensive evaluations show superior final surplus, reduced ruin probability, and favorable computational efficiency compared with traditional methods, albeit with tail-accurate improvements still needed. The framework offers a scalable, robust approach for modern multi-line insurance operations and catastrophe-risk management, with potential to enhance risk-sharing strategies under evolving market conditions.

Abstract

Reinsurance optimization is critical for insurers to manage risk exposure, ensure financial stability, and maintain solvency. Traditional approaches often struggle with dynamic claim distributions, high-dimensional constraints, and evolving market conditions. This paper introduces a novel hybrid framework that integrates {Generative Models}, specifically Variational Autoencoders (VAEs), with {Reinforcement Learning (RL)} using Proximal Policy Optimization (PPO). The framework enables dynamic and scalable optimization of reinsurance strategies by combining the generative modeling of complex claim distributions with the adaptive decision-making capabilities of reinforcement learning. The VAE component generates synthetic claims, including rare and catastrophic events, addressing data scarcity and variability, while the PPO algorithm dynamically adjusts reinsurance parameters to maximize surplus and minimize ruin probability. The framework's performance is validated through extensive experiments, including out-of-sample testing, stress-testing scenarios (e.g., pandemic impacts, catastrophic events), and scalability analysis across portfolio sizes. Results demonstrate its superior adaptability, scalability, and robustness compared to traditional optimization techniques, achieving higher final surpluses and computational efficiency. Key contributions include the development of a hybrid approach for high-dimensional optimization, dynamic reinsurance parameterization, and validation against stochastic claim distributions. The proposed framework offers a transformative solution for modern reinsurance challenges, with potential applications in multi-line insurance operations, catastrophe modeling, and risk-sharing strategy design.

Figures (9)

• Figure 1: Variational Autoencoder (VAE) architecture for generating synthetic claims. The encoder maps historical claims to a latent space, while the decoder reconstructs realistic synthetic claims Kingma_and_Welling2014Higgins2017.
• Figure 2: Framework Overview: Interaction workflow between the VAE and RL components. The VAE generates synthetic claims, which are processed by the RL environment. The RL agent observes states, executes actions, and receives rewards to optimize reinsurance strategies dynamically.
• Figure 3: Surplus Trajectory Over Time. Early fluctuations diminish as the PPO agent stabilizes surplus above the ruin threshold (red dashed line).
• Figure 4: Comparison of Training Data and Generated Data. Discrepancies in the tail regions indicate the model's limitations in capturing extreme events.
• Figure 5: Training vs. Generated Data for the Lognormal Distribution. The generated data underrepresents tail regions.