Return fraud is any abuse of the returns process: sending back a worn item as new, returning a different or damaged product, claiming an empty box, or serially exploiting free-return policies. It hides inside legitimate return volume, which is what makes it expensive and what makes the obvious fix dangerous. Tighten policy for everyone and you punish the honest majority who decide where to shop based on easy returns. The brands that win treat fraud prevention as a targeting problem: verify the risky returns, stay invisible to everyone else.
The forms return fraud takes
Wardrobing: buy, use, return as new; endemic in fashion and event-driven purchases. Item substitution: a different, older, or damaged item comes back in the right box. Empty-box and missing-item claims. Bracketing abuse: systematic over-ordering with intent to return beyond any genuine fit need. And policy arbitrage: serial returners who treat the return window as a free rental period. Each form leaks margin differently, and each responds to a different control.
The two-layer prevention model
Layer one: verification inside the returns portal. The single highest-leverage control is checking the item before a label is issued, not after it arrives at the dock. Photo verification asks the shopper to photograph the item during the return request; AI computer vision compares the images against the product and the stated reason, catching substitutions, wear, and condition mismatches at the moment they are cheapest to stop. On Route Returns, this condition-check layer eliminates up to 99% of returns fraud.
Layer two: identity and behavior risk. Some fraud is invisible at the item level and obvious at the account level: return frequency, refund-to-purchase ratios, address and payment patterns. Risk scoring on those signals lets you route high-risk requests to manual review while low-risk returns flow through automatically. The decision stays human: AI assesses and scores, and the merchant's team approves or denies.
Five ways to cut fraud without punishing good customers
1. Verify selectively, not universally. Apply photo requirements and review friction by risk signal, item value, and category. A first-time customer returning a $30 tee should glide; a tenth return of a $400 dress two days after a gala can earn scrutiny.
2. Use condition data to fast-track resolutions. Verified-good items qualify for instant exchanges and fast refunds. Unverifiable or suspect returns move to refund-on-receipt. Honest shoppers get faster outcomes than they would under a one-speed policy.
3. Watch accounts, not just returns. Serial abuse only shows up in history. Account-level flags catch the 15th fraudulent return that looks identical to a legitimate one in isolation.
4. Redefine free returns instead of removing them. Free returns drive purchases: 82% of shoppers say easy returns influence trying a new brand, per Route's 2026 consumer research. Consumer-funded models square the circle: shoppers opt into Free Returns plus Package Protection at checkout, so the benefit exists without inviting unlimited-cost abuse.
5. Close the exceptions deliberately. Final-sale items, high-theft SKUs, and repeat-abuse accounts should follow explicit rules, not support-agent improvisation. Policy consistency is itself a fraud control; improvised exceptions get found and farmed.
The proof it can be invisible
The fear with fraud controls is friction. Done at the portal layer with AI doing the checking, honest shoppers mostly never notice it. Mike Wu, Director of Ecommerce at Monos, on Route Returns: “better fraudulent controls... with the use of their AI.” The controls tightened and the experience did not. That combination, 99% less returns fraud without a heavier process for good customers, is the standard to hold any returns platform to; our buyer's guide covers how to evaluate it.
