According to Appriss Retail, U.S. retailers saw more than $369B worth of returned products, and $18-24 billion of those returns were fraudulent. There are lots of ways that return fraud is being perpetrated ranging from fake receipts to cloned gift cards. But one of the most common schemes involves buying an item and returning a fake item in its place. Unfortunately, the counterfeit items are becoming so convincing that it’s often nearly impossible to distinguish them from the genuine articles.
Antonio Linares, the operator of Fake Education, a site dedicated to preventing fraud told The Fashion Law, “We are now at the point where the fakes are almost identical to the real thing … where they are almost 99 percent identical.”
Given that the quality of the counterfeits is rapidly improving, it’s becoming increasingly unrealistic to expect store clerks tasked with handling returns to be able to spot the difference. Therefore, strategies aimed at preventing return fraud need to move away from spotting phony receipts or merchandise and instead focus on spotting the people who commit the crimes. And facial recognition technology may provide the best means of doing so.
How Face Recognition Prevents Fraud
According to a recent Apriss study, 6.5 percent of returns (and 9% of holiday returns) are fraudulent. The most important thing to understand about individuals who commit return fraud is that many are serial offenders known as organized retail criminals (ORC). And unfortunately, according to NRF, 75% of retailers report that organized retail crime is on the rise.
But fortunately, retailers can use facial recognition to recognize, in real time, individuals who have previously committed fraud against them. After validating the identity of the individual from the alert, employees can offer great customer service to prevent another theft or fraudulent return.