Understanding Return Fraud in Supply Chain Operations

One of our merchants noticed a quiet but scary spike in refunds. After digging in, they realized they lost over $15K last quarter. Mostly on <$100 items from returns that were refunded before anyone opened the box. It wasn’t chargebacks. It was a pattern of return label abuse: 1. Shopper prints a label, 2. Package arrives, looks normal, 3. Refund auto-triggers on scan, 4. QC opens it… knock-off hoodie inside And this wasn’t a one-off. Returns fraud is up 29% YoY in apparel, per Shopify Plus benchmark data. I can’t believe that many RMS flows still refund on first carrier scan, which was designed for CX speed in like 2018, long before swap-for-fake rings exploded in Telegram groups. So what do you do? Well, this merchant turned on Frate Returns' AI-powered image verification (shoutout Bailey). It shifts the QC to "immediate", instead of days later. Fraud refunds dropped 73% in 30 days. NPS held steady, same fast refund experience. Returns fraud is evolving quickly. If you’ve seen sketchy return behavior (empty boxes, fake swaps, or odd refund triggers) happy to share what we’ve learned.

Retailers hate returns. 🚨Return-fraud is now a $103 B problem. Merchants can’t keep treating “free, no-questions-asked” returns as a loss-leader anymore. The latest BI piece (link in first comment) makes it painfully clear why. When even otherwise honest shoppers are bragging on TikTok about “wardrobing,” swapping items, or shipping back empty boxes, we’re not talking about edge-case crime. This is an epidemic that erodes margins, skews demand-planning data, and ultimately forces every customer to pay more. Why does this keep happening? Because most e-commerce and returns workflows still rely on weak signals — a shipping label and a self-asserted identity. Fraudsters exploit that gap at scale. Here's how to flip the script: 🔐 Biometric authentication at checkout ties every purchase (and every return) to a living, breathing human — not just a tracking number. 🤖 Real-time risk scoring & network tokenization flag anomalies before a refund is released, slashing “empty box” and “item switch” claims. 🛡️ Chargeback-insurance & AI-driven dispute workflows turn what used to be a sunk cost into a contained, auditable process. Early pilots show that when merchants add biometric step-up for high-risk returns, fraudulent claims drop 40-60 %, and genuine customers still breeze through in under two seconds. Return policies shouldn’t turn retailers into unsecured lenders — and they don’t have to. Identity-first payments can protect revenue and keep the honest majority delighted. #ecommerce #biometrics #payments Wink

One of our partner brands asked me to investigate a suspicious cluster–and we found more than two shoplifting articles tied to a single customer profile. What stood out wasn't just the old-school in-store theft and how those tactics have morphed online into what I call "digital shoplifting." Here's the story in four acts: Bots vs. Hype (2010s–Present) Remember the Kylie Lip Kit craze (I'm aging myself here… those were the days)? Automated bots wiped out stock in seconds. We saw the same playbook on Supreme drops, concert tickets, and even limited-edition sneakers–bots overwhelmed sites and left honest customers empty-handed. Return Floodgates Free, lenient return policies became a double-edged sword during the pandemic. U.S. returns hit $428 billion in 2020, with nearly 6% outright fraud. Opportunists treated refund windows like loopholes, turning returns into a stealthy shoplifting channel. "Inflation Hack" Rationales A Fortune study found that half of affluent Gen Zs and millennials admit to "digital shoplifting," often shrugging it off as an "inflation hack." With coupon stacking, inflated refund claims, and the cloak of online anonymity, bad actors feel emboldened, and brands feel the sting. The "Item Not Received" / Return Fraud Scam The most insidious trick is false "never received," "empty box," "Lost in Transit," or "It's delivered to your warehouse" claims. Fraudsters pocket the goods and the refund. Industry data shows that 32% of fraud attacks exploit this tactic, silently draining margins. What's the fix? You wouldn't let LP teams roam your website's aisles–so why leave your checkout and return process unprotected? Applying machine learning or a platform like Yofi with behavioral signals allows you to flag risky transactions before they hit the refund or checkout queue. Let's treat our digital storefronts with the same rigor as our warehouses. In a world where "I never received it" is the new shoplifting shout, data is your best Loss Prevention specialist. How are you defending your brand against digital shoplifting? #ecommerce #fraudprevention #onlineshopping #retailtech