Dynamic Risk Assessment and Intelligent Decision Support System for Cross-border Payments Based on Deep Reinforcement Learning

Abstract

Cross-border payment systems face unprecedented challenges in maintaining security while enabling seamless international transactions. Traditional risk assessment methods demonstrate limited effectiveness in handling real-time decision-making requirements within complex multi-jurisdictional environments. This research presents a novel framework integrating multi-agent deep reinforcement learning with multi-modal data sources to develop an intelligent decision support system for cross-border payment risk assessment. Our approach combines transaction pattern analysis, sentiment evaluation from financial news sources, and macroeconomic indicators to create a comprehensive risk evaluation mechanism. The proposed system employs Deep Q-Networks and Multi-Agent Deep Deterministic Policy Gradient algorithms to optimize risk-adjusted decision outcomes. Experimental validation demonstrates significant improvements in prediction accuracy compared to conventional methods, achieving 94.7% precision in fraud detection while reducing false positive rates by 23.8%. The system processes real-time transaction data with average latency of 12.3 milliseconds, meeting stringent operational requirements for high-frequency payment environments. Integration of sentiment analysis contributes to enhanced risk pattern recognition, particularly in volatile economic conditions. The research contributes to advancing automated financial risk management through intelligent multi-agent systems capable of adapting to evolving threat landscapes.