How One Fix in Payment Screening Could Save Billions and Keep Borders Open for Honest Trade

Think of a scenario where a factory worker in rural Vietnam sends her hard-earned paycheck home, only to face the transfer stall into a compliance black hole because her name, written in her local script, confuses an outdated bank filter. Similar occurrences in daily cross-border payments may result in a crisis for individuals and corporations. According to a 2024 report, global banks spent $34.7 billion just on financial crime inspections, with sanctions screening consuming about 20% of that amount, much of it chasing false alarms that obstruct the works and let real hazards slip by. It's a chaotic situation that is not only affecting earnings but also stopping the flow of everyday capital that strengthens small-scale businesses, remittances, and global supply chains. Yet, unwinding this tangle are payment architects like Senthil Nathan, whose innovative executions in handling non-Latin scripts enabled legitimate funds to move faster while improving the search for bad performers.

The payments space works on trust, but for years, that trust has been tested by a persistent issue of how to spot a sanctioned name when it's written in characters that don't align with Western tech. Consider Chinese hanzi, Arabic right-to-left curls, or Japanese kanji scripts carrying the weight of entire cultures, but confuse the rigid algorithms most banks use. These tools, built decades ago for Latin letters, depend on rough phonetic guesses or black-box transliterations that mangle meanings. A 2023 Celent analysis highlighted error rates at over 42% for Chinese names alone, turning simple matches into a game of telephone gone wrong. False positives, the naive hits that scream "threat," pile up like junk mail, striking 90–95% of alerts according to PwC data, and costing the world economy more than $274 billion annually in wasted compliance hours. On the other side, false negatives are the real problems, these overlooked risks that support everything from arms deals to human trafficking, with U.S. regulators alone slapping $1.5 billion in fines in early 2023 for such missteps.

Banks know this problem well. They've hired huge teams with thousands of analysts working nonstop to review payment alerts, but it still feels like trying to empty a sinking boat with a teaspoon. The people who suffer most are regular customers, for whom late money transfers indicate some families can't pay for basic needs, and small exporters in Asia or Latin America lose business because their payments get stuck in "review."

This isn't just a personal inconvenience; it slows down trade worldwide. McKinsey estimates that cross-border payment issues cut 1–2% from global GDP each year, largely due to screening delays. And socially, it makes inequality worse. Many diaspora communities, particularly those who use non-Latin scripts, get labeled as "high risk" just because of their names, pushing them toward informal money channels where real criminal activity can be concealed.

As Nathan said in a recent interview about the project: "We weren't just rectifying code. We were clearing paths for people whose stories get lost in translation; people working hard and sending their efforts across borders without apology."

Nathan spotted the rot early in his role overseeing high-value wire and cross-border rails. Legacy systems treated non-Latin inputs like puzzles with missing parts, forcing crude workarounds that were either over-flagged or under-detected. Drawing on his years of architecting gateways in Singapore and beyond, he zeroed in on integrating the solution right where it mattered: inside the screening pipeline itself, before the data even hit the alert queue. No more outsourcing to unreliable third-party converters; instead, a custom engine that learns the nuances of scripts like Pinyin tones for Mandarin or Hepburn romanization for Japanese. Trained on millions of real payment messages and official registries, it grasps context, honorifics that shift meanings, and common variants in diaspora spellings, without the guesswork.

Not stopping at just translation, Nathan layered in AI-driven repair, a smart reroute that flags ambiguities and auto-generates enriched variants for instant re-checks, all in real time. Pair that with strict ISO 20022 field rules, assuring legal names, entity IDs, and addresses land in the right spots, and you've got a system that doesn't just transliterate but verifies. "The goal was idempotent accuracy," Nathan recalls, "meaning once a name crosses the line, it stays true, no matter the script or the chaos of a peak-hour surge." This wasn't some lab experiment; it went live, handling billions in flows, processing over 1.4 billion non-Latin payments without a single confirmed slip tied to script mishandling.

Moreover, starting with the commercial grind, banks using this approach saw false positives on script-heavy payments drop above 70%, freeing analysts to chase genuine leads instead of dead ends. Straight-through processing, payments zipping end-to-end without human barriers, jumped into the high 99% range, an advantage for corporates juggling supplier invoices across Shanghai factories and Sao Paulo warehouses. In retail, faster clearances imply remittances from a nurse in Toronto to kin in Manila arrive same-day, not delayed in limbo. One mid-sized U.S. exporter to Southeast Asia reported shaving two days off payment cycles, opening $500,000 in tied-up cash per quarter, multiplied across thousands of companies. That's real power for multinational trade.

By closing what Nathan calls the "script gap," this work undercuts the evasion tricks sanctioned players' value, hiding behind cultural mismatches that old systems ignore. Fewer misses mean stronger barriers against funding conflicts or corruption, aligning with G20 pushes for transparent flows. In emerging markets, where non-Latin scripts dominate 60% of transactions according to BIS data, it levels the field. For instance, small vendors in Cairo or Mumbai can bank globally without the bias of botched transliterations. Economically, Deloitte estimated ties reduced false positives to $10–15 billion in annual savings industry-wide, cash that could circle back to innovation or community lending. Nathan's solution even nudged SWIFT's 2024 ISO guidance, which now stresses original-script handling to avoid exactly these pitfalls.

Of course, rolling this out wasn't an easy endeavour, where Nathan's team struggled with data insufficiency, scarce training sets for rare dialects, and regulatory nitpicks demanding audit-proof explainability. They iterated through shadow runs, testing against live volumes without risking a single customer's dime. Peers in the industry, from fintech upstarts to legacy giants, started borrowing the blueprint; one European correspondent bank adapted it for Cyrillic-heavy Eastern corridors, cutting their alert backlog by half.

From a futuristic point of view, Senthil Nathan's script-savvy engine stands as a cornerstone for what's next in borderless finance. With ISO 20022 rolling out fully by late 2025, expect it to underpin richer data flows that make evasion even tougher, think AI spotting layered shells in real time, or blockchain ledgers carrying native scripts without loss. For international markets, this suggests smoother $150 trillion in yearly cross-border volumes, per McKinsey, with fewer frictions impeding growth in places like Africa or the Middle East. Retail and commercial players gain predictable speed, while society reaps safer channels that don't penalise the innocent. In a world where every delayed dollar hits a family or a factory, this work doesn't just patch a pit; it paves wider roads for trade that lifts everyone. As borders blur and scripts mingle, advancements like Nathan's make sure the global pulse of payments beats steady and fair.