Agentic AI for Next-Generation Cross-Border Payments: Contextual Learning in Transaction Routing

Abstract

Next-generation cross-border payment features, the contextualization of routing policies, and the consequent reduc- tion of latency, cost, foreign exchange risk, and opacity are more than just buzzwords; they actually matter. When transferring funds from one country to another—say, when a foreign worker sends money home—transaction time, transfer fees, costs in- curred from foreign exchange trading, and the transparency of these costs all play an important role. Current networks often struggle to meet expectations for any of these variables. However, routing policies for some corridors currently utilize autonomously learned routes capable of adapting behavior according to contex- tual factors. If this contextual knowledge could be expanded to all corridors, it is likely that transferred funds would spend less time in transit, cost less to send, and provide the receiver with a more favorable exchange rate. Theoretically, contextualization might be achieved through contextualized reinforcement learning or by means of transfer learning across corridors. In either case, improving cross-border payment routing is about more than simply enhancing the user experience; it is also about enabling better compliance with local rules and regulations—and, ultimately, about operating safely within the constraints of the overall financial system. These objectives need not be at odds with contextualized routing policy learning, since upsides for latency, cost, foreign exchange risk, and transparency likely result in increased transaction volume and higher net income.