Optimizing risk transfer in dynamic insurance networks: A graph-based reinforcement learning framework

The optimization of risk transfer within interconnected insurance networks has become increasingly complex, requiring models that effectively capture dynamic financial environments. This paper introduces a mathematical framework designed to optimize risk transfer in dynamic insurance networks by integrating graph neural networks with multi-agent deep reinforcement learning. The model accounts for key complexities, such as stochastic capital flows, heterogeneous risk appetites, dynamically correlated risks, and endogenous systemic risk feedback, within an evolving network structure. A graph neural network is employed to learn the time-dependent correlation structure of insurer losses directly from the network, while a proximal policy optimization algorithm is used to dynamically optimize risk transfer policies. To ensure robustness under extreme uncertainty, the framework utilizes Entropic Value-at-Risk as its core risk measure, providing a conservative and coherent optimization objective aligned with regulatory standards. An illustrative example demonstrates the model’s ability to enhance network stability, reduce tail risks, and support financial resilience. These results highlight the model’s potential to transform risk transfer strategies within interconnected insurance networks, offering valuable insights that can inform both insurance risk management practices and regulatory frameworks. Future validation through extensive numerical experiments is proposed to further assess the model’s effectiveness in real-world scenarios.