Babel Street Match Helps Banks Meet Verification of Payee Mandates

Erroneous electronic funds transfers (EFTs) happen all the time. Often, users simply misdirect payments. You mean to reimburse your friend Jane Doe for your share of a dinner out. Instead, a stranger named Jane Does finds a stray €50 floating around her bank account. Just as often, bad EFTs result from scams. A criminal poses as your grandson Jimmy. “Jimmy” tells you that he has been arrested. He’s innocent and needs €3,500 to post bail. You inadvertently transfer money to a scammer’s account.

These are not rare errors. Moneyhub estimates that, in the United Kingdom, nearly 9% percent of the population has accidentally transferred money to the wrong person.[1] Eight percent has received unexpected payments from persons unknown to them.[2] And PYMNTS reports that in 2024, social engineering scams such as the one discussed above surpassed digital payment crimes as the leading cause of fraud in United States banks.[3] (Unlike social engineering scams — which target people — digital payment crimes exploit vulnerabilities in payment systems and other technologies.)

Wanting to limit instances of erroneous and unauthorized EFTs, the European Commission’s Instant Payments Regulation now requires “Verification of Payee” (VoP) processes. The Regulation mandates banks and other payment service providers (PSPs) verify that a payee’s name matches the name on the account into which funds are being deposited.

The VoP initiative builds on existing national mandates such as the United Kingdom’s Confirmation of Payee law and the Netherlands’ IBAN Name Check requirement. Banks and other PSPs in countries that use the Euro must have a VoP system in place by October 9, 2025. All other countries in Europe’s Single Euro Payments Area (SEPA) have until mid-2027 to comply.

How do VoP processes work?

The payer’s bank queries the payee’s bank to see if the payee’s name matches the name associated with the account’s IBAN. Within seconds, the bank must notify the payer of whether the names match, whether they don’t match, or whether they closely match. This notification may take the form of an icon appearing on the payer’s screen. For example, a green icon may be used to indicate that an exact match has occurred, and it’s safe to proceed with the transaction. Other colors may be used to indicate a mismatch or a close match. In cases of close matches, (“Thomas Johnson rather than Thomas Andrew Johnson,” “Jane Doe” rather than “Jane Does”) the bank may choose to present alternative names. The payor then decides whether to proceed with the transaction.

VoP processes will do much to limit both fraud and misdirected transfers. In the case of scams, payee names will simply not match the name of the account holder. If a woman wants to send cash to her son, Thomas A. Johson, and the system learns that the payee’s IBAN number is associated with a person named Emil Andersen, there is no match. Someone is likely trying to scam this woman.

But what about close matches? What if a woman is trying to transfer money to “Thomas A. Johnson,” and that name is only a close match to named account holder “Thomas Andrew Johnson?” The bank may choose to present the payor with likely matches. Alternately, the woman may simply contact her son to learn that he used his full middle name when opening his account. She should remit money to “Thomas Andrew Johnson.”

Improving VoP with Babel Street Match

Clearly, VoP processes in Europe — along with similar initiatives arising from the banking industries in Australia and New Zealand, and from Nacha in the United States — require improved name matching capabilities.

Babel Street Match was designed to meet financial institutions’ (FIs) need for fast, accurate, multilingual name matching at scale. It works across dozens of languages and a variety of different scripts — including Arabic, Cyrillic, Chinese ideographs, and Japanese Kanji, among others — to compare, match, and score the names of individuals and organizations. In doing so, it reduces false positives by up to 90 percent, along with the concurrent need for manual investigation.

Match’s parameters are highly tunable to fit VoP risk profiles. Financial institutions can weight different scoring penalties — such as those for missing name components, out-of-order names, initials, and gender mismatch. An FI may choose to automatically consider any potential match with a score of, for example, less than 75% accuracy a mismatch. Alternately, it may choose to automatically accept any match with a score of 85% accuracy or higher.

These capabilities help FIs more quickly and accurately comply with VoP mandates. They also clarify match status for consumers — protecting them from scams and from accidentally misdirecting payments.

Need help with VoP? Consider Babel Street Match.

Endnotes

1. Moneyhub, “Nearly 1 in 10 Have Accidentally Made a Payment to the Wrong Person,” October 2024, https://www.moneyhub.com/press-blog/2024/10/25/nearly-1-in-10-have-accidentally-made-a-payment-to-the-wrong-person?utm_source=chatgpt.com

2. Ibid

3. PYMNTS, “Scam-Related Fraud Jumped 56% in 2024, Surpassing Digital Payment Crimes,” December 2024, https://www.pymnts.com/news/security-and-risk/2024/scam-related-fraud-jumped-56percent-surpassing-digital-payment-crimes/?utm_source=chatgpt.com

Disclaimer:

All names, companies, and incidents portrayed in this document are fictitious. No identification with actual persons (living or deceased), places, companies, and products are intended or should be inferred.