Multi-Risk Stress-Testing of Insurers using Multi-Agent Reinforcement Learning

Music City Center 

Insurers are increasingly vulnerable to multiple sources of risks, including climate-related risks and market risks. Multi-risk stress testing models that simultaneously test a firm's likelihood to survive multiple catastrophic events are scarce, and existing models do not properly account for dynamic feedback due to agents continual interactions with the environment. Whereas there exists strong interdependence between human actions, the environment, and risks.
This study develops a multi-risk stress testing model with dynamic feedback mechanisms based on multi-agent reinforcement learning (MARL). The model is employed to evaluate the vulnerability and resilience of property and casualty (P & C) insurers in the U. S to catastrophic windstorm and market risks. Market risk is introduced in the model through volatility in returns from investing premiums in the bond and equity market and through the correlation between premium rates and the returns on investment. Results using new and unique firm and macro-level data sets offer deeper understanding of P & C insurers' exposure to multi-risks with significant regulatory implications.

Insurance

Catastrophic risks

Stress-testing

Multi-Agent Reinforcement Learning 

Business and Economic Statistics Section