In cross-border payments, getting paid isn’t just a matter of when — it’s a matter of how.
News broke onWednesday (Dec. 11) that financial platform Loop, in collaboration with EQ Bank, launched the Loop Global Visa Card to enable small- to medium-sized businesses (SMBs) to spend and settle credit balances in multiple currencies. It is one example of firms looking to expand internationally turning to currency matching to do so.
With the complexities of fluctuating exchange rates, disparate financial systems and opaque fees, businesses engaging in cross-border B2B payments often find themselves navigating a minefield.
The inefficiencies can be crippling. Currency conversion fees, unpredictable exchange rate movements and delays caused by intermediaries often erode margins and introduce operational uncertainty, a situation that only gets compounded for SMBs, which lack the resources and financial expertise to navigate the foreign exchange (FX) markets effectively.
Against this backdrop, innovations in currency matching, as well as a shift toward virtual cards, are emerging as a critical lever for companies looking to unlock competitive advantages in this high-stakes arena.
Read more: Can Payments Innovations Solve U.S. Merchants’ Top 5 Cross-Border Challenges?
At its core, currency matching seeks to optimize cross-border transactions by identifying and pairing incoming and outgoing payments in the same currency. This approach helps minimize the need for conversion, ultimately reducing both costs and exposure to exchange rate volatility.
By leveraging advanced data analytics and artificial intelligence (AI), currency matching platforms can now automate these processes, aligning transactions in real time and ensuring efficiency at scale. For instance, a U.S.-based exporter receiving payments in euros can offset these inflows against outgoing payments to suppliers in the eurozone. The result? Fewer currency conversions, lower fees and a streamlined payment workflow.
These ongoing advancements in technology are pivotal in making currency matching accessible and scalable. Real-time payment networks, APIs and AI-driven FX management tools are increasingly working to transform a once labor-intensive process into an automated solution.
A key innovation is the integration of machine learning algorithms that predict cash flow patterns and recommend optimal currency matching strategies. For treasurers and CFOs, this functionality translates into better liquidity management and more predictable financial outcomes.
“Many people are unaware of more viable, cost-effective and efficient methods for making cross-border payments,” Dean M. Leavitt, founder and CEO of Boost Payment Solutions, told PYMNTS, noting that “cross-border payments for enterprise-level B2B transactions are inherently complex.”
Read more: What on-Soil Requests Mean for Cross-Border Payments Compliance
In an increasingly interconnected global economy, businesses cannot afford to leave money on the table when it comes to cross-border payments.
“If you look at the cross-border payment space over the last five years, the payment volumes have grown,” Chandana Thanthrige of Bank of America told PYMNTS.
But as PYMNTS Intelligence’s “Cross-Border Sales and the Challenge of Failed Payments” revealed, faulty cross-border payments cost U.S. merchants at least $3.8 billion in sales last year. Additionally, 70% of U.S. firms experienced higher rates of failed payments in cross-border sales compared to domestic sales.
“The more parties there are in a transaction, the more risk there is … information is not passed along in the same exact fields as it moves between providers,” Nium Chief Payments Officer Alex Johnson told PYMNTS. “As you eliminate some of the intermediary layers of a transaction, there’s less opaqueness, and the transactions are more cost-effective. It’s not just about the speed of the transaction; it’s about the transparency and knowing where it is at every point of time.”
Real-time currency matching isn’t the only factor at play in helping build a more streamlined, simplified and cost effective cross-border B2B ecosystem. Regulators and policymakers also have a role to play in fostering a competitive, transparent cross-border payments ecosystem. For example, global initiatives like ISO 20022, aimed at standardizing payment messaging, could further facilitate the adoption of currency matching and related innovations.
As PYMNTS’ Karen Webster noted in an earlier interview, the focus on cross-border innovation needs to be on solving key frictions: moving money securely and safely, providing transparency throughout the process and optimizing the economics of cross-border transactions.
Seamless and efficient cross-border payments have become more important as businesses look abroad for new markets and customers, according to the PYMNTS Intelligence and Citi collaboration, “The Treasury Management Playbook: Spotlight on Cross-Border Payments.”
Agentic artificial intelligence (AI) promises to improve operational efficiencies and the customer experience offered by enterprises.
The advanced technology is finding applications in loan underwriting and fraud detection, and now it’s moving across borders.
TerraPay Co-Founder and Chief Operating Officer Ram Sundaram told PYMNTS as part of the “What’s Next in Payments” series focused on exploring AI’s use in banking and by FinTechs that automated decision making and streamlined processes will continue to transform global money movement, especially as faster payments gain ground in cross-border transactions. That’s the inexorable trend, but as Sundaram put it, there’s still room, and a necessity, to have some human interaction in the mix.
In terms of global fund flows, TerraPay’s single connection ties more than 3.7 billion mobile wallets together across 200 sending and 144 receiving countries, touching 7.5 billion bank accounts. As one might imagine, coordinating and enabling the transactions is complex.
“Obviously, in the best-case scenario, everything goes smoothly, but when things are not going smoothly, that’s when the customer queries come in,” Sundaram said.
It’s no easy task to find out straight away where a transaction is, as analysts and representatives at the company have to look at logs and query partner systems.
“A lot of that work is done manually,” said Sundaram, who added that the agents “know the corridors and the markets that they are working in, but it still takes some time.”
TerraPay is using AI models with machine learning to bolster customer support and automate tasks as financial institutions (TerraPay’s client base) send payments in real time, and those payments are processed into local markets’ beneficiary banks.
“We still don’t trust [AI models] to let them respond to the customer straight away, but we can do the analysis, and then that gets reviewed by an agent who decides if [information] is accurate or not and then sends it off,” Sundaram said.
The same principles are guiding AI models and company practices to improve technical and security operations, analyzing and categorizing anomalous transactions and automating integrations with partner firms.
“Compliance is an issue where there is a lot of review needed of the alerts, and we are using [AI models] to speed up those processes,” Sundaram said.
Asked by PYMNTS about how agentic AI can be harnessed, he said: “In financial services, you can’t take chances on technology like this, which has the freedom to go wrong. You have to be careful about making sure that it’s 100% reliable before we can let things run entirely by automation.”
Agentic AI also remains pricey. For example, OpenAI is charging $20,000 a month for its specialized agents. However, Sundaram said the industry will become commoditized quickly, which will lower prices, and some open-source offerings are capable.
“There’s a fire hose of news about breakthroughs and new ideas and new ways of doing things that are coming out on a daily basis,” he said.
Data underpins it all, and Sundaram told PYMNTS that no matter what the application, the information fed into the models must be clean. Most organizations have a range of data sitting in different intra-company silos, and those silos need to come down.
In addition, the data must be structured so that it is accessible and can be synthesized by the models. Many firms may have more than 1,000 software-as-a-service (SaaS) resources to which they are subscribed but are not accurately tracked or monitored.
“Every database is separated, each one sitting somewhere else,” he said.
The days of stitching together those separate SaaS offerings to run an enterprise are ending, he said, and we’re headed to a future when data is collected in one place.
AI models and agentic AI “are extensions of what we’ve always valued at TerraPay, which means building the most efficient infrastructure possible in order to make sure that transactions are processed safely, quickly and affordably,” Sundaram told PYMNTS. “We see AI and [AI models] as powerful tools that help us scale all this very quickly while making sure we build more and more efficiency into the system.”
