Name matching fails: the real-world consequences
Who needs name matching?
Ask Taylor Swift. No, not the singer. The 30-something photographer from Seattle. He’d like to stop getting her fan mail. Or Chris Evans, the American actor (Captain America, Avengers), and Chris Evans, the British television presenter (Top Gear). Or oncologist Donald Trump, CEO of the Inova Schar Cancer Institute in Falls Church, Va.
There are nearly 8 billion people on this planet. Some of us are going to have the same name.
This situation challenges financial institutions, law enforcement agencies, national intelligence organizations, national security organizations, healthcare systems, and others. Linking the right records to the right “Taylor Swift” or “Chris Evans” is vitally important to watchlist screening, anti-money laundering efforts, and even to providing sound medical treatment.
While coping with many different people having the same name is one hassle, organizations must also manage an inverse situation: the misspellings, aliases, nicknames, use of initials, and language differences that can leave a single person with several different names. Organizations need a way to determine whether “Giuseppe Bianchi,” “G. Bianchi,” “Joseph Bianchi,” and “Joseph White” are — or at least may be — the same man.
Inadequate name matching can have disastrous real-world consequences. We’ve all heard the stories of young children appearing on no-fly lists because they have names similar to those of suspected terrorists. But typically, the consequences of failed name matching procedures are far more significant than delayed vacations. The U.S. Department of the Treasury’s Office of Foreign Assets Control (OFAC) may levy heavy fines against companies doing business with entities appearing on various sanctions lists. (In worst-case scenarios, OFAC can even seize businesses.) Public agencies may disburse taxpayer money to dead people. Innocent people may go to jail.
And your corporate reputation may take a hit. No company wants to be in the news for doing business with unpalatable entities. You don’t want to be seen helping Russia in Ukraine.
Let’s take a closer look at the real-world consequences of suboptimal name matching.
In 2018, OFAC fined JPMorgan Chase Bank more than $5.2 million, in part because the bank “failed to identify customer names … as potential matches to similar or identical names” appearing on OFAC lists of people believed to be undermining the country’s national security and foreign policy goals.
Other banks have yet to learn from JPMorgan’s mistake. In late 2021, OFAC fined TD Bank for violating sanctions against North Korea nationals. The problem arose when bank employees incorrectly labeled account holders as coming from “Korea” or “South Korea” rather than from “North Korea.”
In early 2022, Airbnb Payments agreed to pay more than $91,000 to settle OFAC charges that it had enabled home-stay payments to Cuban nationals from guests traveling to Cuba for unauthorized reasons. Amazon.com was fined more than $134,000 for sanctions screening failures that left it providing services to persons sanctioned by OFAC living in Crimea, Iran, and Syria.
In the public sector
A recent audit by the Inspector General of the Veterans Benefit Administration found that the agency’s automated name matching systems failed to match with Social Security Administration death records. The failure led to at least 43 cases of improper payments, 29 of which continued for seven months or more. This means that certain monthly payments disbursed by the VA (such as disability compensation) continued long after the beneficiary had died.
In law enforcement
The optics of imprisoning a Black man for a crime committed by a white man are chilling. But that’s exactly what happened earlier this year in Nevada. Police stopped Shane Lee Brown, a 25-year-old Black man, for a routine traffic violation. Brown couldn’t produce his license, so police ran his name. The name “Shane Brown” was associated with a felony warrant. But unbeknownst to the police, the Shane Brown facing firearms charges was 49 years old and white. The 25-year-old Brown was arrested, and spent six days in jail.
This is not an isolated case. The Transportation Security Administration stopped a California woman traveling to Mexico. Bethany Farber spent 13 days in a Los Angeles jail on charges of criminal mischief. These charges had actually been levied against a woman named Bethany Farber living in Texas, a state the first Bethany Farber had never even visited.
The human costs of these arrests is apparent. What may be less so is the cost to taxpayers. Brown is suing two Nevada police departments for upwards of $500,000. Farber is suing the Los Angeles Police Department for civil right violations, wrongful arrest, and negligence. When plaintiffs prevail in lawsuits against the government, taxpayers typically foot the bill.
A better way
Accurate, precise name matching is vital to anti-fraud efforts, government intelligence, law enforcement, and other activities. The Rosette text analytics platform leverages machine learning and artificial intelligence to ease name matching processes. Available on premises or in the cloud, it quickly coalesces identities, employing information beyond names — ages, addresses, and places of birth among them — to distinguish one “Joseph White” from another “Joseph White.” Conversely, it examines name variations, misspellings, nicknames, initials, titles, and language differences to determine that “Joseph White” and ““Giuseppe Bianchi” may be the same person. These capabilities keep companies from the fines, lawsuits, and reputational damage that arise from imprecise name matching.
Start improving name matching today. Read more about Rosette.
 https://www.huffpost.com/entry/man-named-taylor-swift_n_563d1408e4b0411d307118c1#. “This guy’s name is Taylor Swift, and it kinda sucks for him.” 2015 ↩
 https://home.treasury.gov/policy-issues/financial-sanctions/recent-actions/20181005 “Settlement Agreement between the U.S. Department of the Treasury’s Office of Foreign Assets Control and JPMorgan Chase Bank, N.A. (JPMC), and finding of violation issued to JPMC.” 2018 ↩
 https://www.reuters.com/business/finance/us-treasury-dept-says-settlement-reached-over-td-banks-sanctions-violations-2021-12-23/ “U.S. Treasury Dept says settlement reached over TD Bank’s sanctions violation.” 2021 ↩
 https://www.reuters.com/business/us-reaches-settlement-with-airbnb-over-cuba-sanctions-violations-2022-01-03/ “U.S. reaches settlement with Airbnb over Cuba sanctions violations.” 2022 ↩
 https://www.jdsupra.com/legalnews/the-mighty-amazon-falls-to-the-ofac-42446/ “The mighty Amazon falls to the OFAC sanctions sword.” 2020 ↩
 https://www.fedweek.com/federal-managers-daily-report/audit-finds-range-of-problems-leading-to-improper-payments-at-va/ “Audit finds range of problems leading to improper payments at VA.” 2022 ↩
 https://www.cnn.com/2022/01/27/us/nevada-man-jailed-misidentified-lawsuit/index.html “A black man was misidentified, arrested, and held for 6 days in place of a white felon twice his age.” 2022 ↩
 https://www.cnn.com/2022/02/23/us/california-woman-jailed-mistaken-same-name-lawsuit/index.html “A California woman spent 13 days in jail after being mistaken for another person with the same name, according to a lawsuit against the City of LA.” 2022 ↩