Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes

ArXi:2504.09396v2 Announce Type: replace-cross We develop a reinforcement learning (RL) framework for insurance loss reserving that formulates reserve setting as a finite-horizon sequential decision problem under claim development uncertainty, macroeconomic stress, and solvency governance. The reserving process is modeled as a Marko Decision Process (MDP) in which reserve adjustments influence future reserve adequacy, capital efficiency, and solvency outcomes.