Deep reinforcement learning for optimizing cross-border payment routing: Bbalancing speed, cost, and regulatory compliance

Cross-border payments remain a critical challenge in global finance, characterized by high costs, delays, and complex regulatory requirements. This research introduces a novel Deep Reinforcement Learning (DRL) framework designed to optimize payment routing across international corridors while balancing competing objectives of transaction speed, cost efficiency, and regulatory compliance. We implement a multi-agent deep Q-network architecture capable of adapting to dynamic financial environments and generating optimal routing paths through correspondent banking networks. Our experimental results demonstrate a 37% reduction in transaction costs and a 42% decrease in settlement times compared to traditional routing methods. Additionally, the model achieves a 98.7% compliance rate with international regulatory standards across various jurisdictions. This research contributes a comprehensive approach for financial institutions to enhance cross-border payment efficiency while maintaining robust compliance with evolving regulatory frameworks. The proposed methodology represents a significant advancement in the application of artificial intelligence to global financial infrastructure.