Reinforcement Learning for Prioritizing Anti-Money Laundering Case Reviews Based on Dynamic Risk Assessment
Addressing the challenges of delayed risk assessment and inflexible prioritization strategies in anti-money laundering reviews, this study investigates reinforcement learning approaches for generating dynamic risk-driven prioritization strategies. It details state-action modeling, reward function design, and policy network training methods, while outlining the model's integration into operational workflows. An experimental environment was constructed using real financial review data. Results demonstrate the model's significant advantages in review efficiency and high-risk identification accuracy, showcasing its potential for online deployment and continuous optimization.
