đ° AI News: AI Is Now Policing Holiday Return Fraud
đ TL;DR Retailers are quietly rolling out AI tools that scan your holiday returns for fraud before your refund is approved. With nearly one in ten returns suspected of fraud, AI is being hired as the new bouncer at the returns desk. đ§ Overview A new AI system is being tested to catch fake or abusive returns as holiday return season surges. A major returns logistics company reports that U S retailers lose tens of billions of dollars each year to return fraud, with almost 16 percent of total retail sales coming back as returns and around 9 percent of those believed to be fraudulent. To fight that, they have built an AI tool that screens returns in real time, looking for suspicious patterns before the money goes back to the customer. Big brands are already piloting it across thousands of physical drop off locations. đ The Announcement A new report highlights that a UPS owned returns service has launched an AI system to flag suspicious returns at scale during the 2025 holiday season. The company handles box free, in store returns for online brands at thousands of locations and has seen how often fraud slips through when refunds are issued quickly. The AI tool is currently being tested with well known fashion and sportswear brands while retailers process an estimated eight hundred fifty billion dollars worth of returns this year. The goal is simple, reduce the roughly seventy six and a half billion dollars lost annually to return fraud without making honest customers feel punished. âïž How It Works âą Pattern spotting - The AI scans return requests from the moment they are started online, looking at timing, frequency, locations, order history and product details. âą Risk scoring - Each return is given a risk score, most sail through automatically, but higher risk returns are flagged for extra checks before the refund is approved. âą Network wide view - Because the system sits across thousands of drop off points, it can spot people trying similar tricks at different locations or across multiple brands.