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

Rahul Modak *

Independent Researcher, USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2023, 08(01), 510-517.
Article DOI: 10.30574/wjaets.2023.8.1.0037
Publication history: 
Received on 22 December 2022; revised on 25 January 2023; accepted on 28 January 2023
Abstract: 
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.
Keywords: 
Deep Reinforcement Learning; Cross-Border Payments; Regulatory Compliance; Multi-Objective Optimization; Financial Networks
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Copyright information: 
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0