The same technology that helps retailers personalize the shopping experience is also helping fraudsters fake the proof needed to exploit their return policies.
Refund and replacement claims, from “this serum gave me a rash” to “the palette arrived completely shattered,” are on the rise, each accompanied by photos and screenshots as proof. The influx is driven by a new returns fraud tactic, one where abusers are doctoring images using AI to make false claims seem more convincing.
Image manipulation with AI is now contributing to the $850 billion returns problem companies experience each year, with almost $77 billion of those returns being fraudulent. AI is helping bad actors polish their stories while revealing a new blind spot for many retailers who are unable to detect when AI is used in claims or to assess its impact on their post‑purchase margins.
Many brands are responding by tightening their policies, adding return fees, and asking customers to jump through more hoops to verify their claims. But this approach creates an even greater liability: losing loyal customers who become frustrated as they’re treated the same as abusers.
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AI Is Stress-Testing Retail Blind Spots
Fake proof is cheap and easy to produce with AI, allowing the same offenders to repeatedly send slightly different, altered photos and stories that look like real customer photos.
With AI, post-purchase abuse can look like:
- Generate or edit damage photos to add cracks, leaks, or broken packaging.
- Creating staged “stolen package” images that show an empty doorstep or an opened box with items missing.
- Producing fake drop‑off receipts as proof that an item was sent back when it never left the customer’s home
- Writing policy-aware emails and chat messages that align with current refund and replacement policies that make it hard to say “no.”
Meanwhile, customer service teams are now serving as fraud investigators. When a bad actor opens a ticket, attaches a photo, and explains what went wrong, reps can no longer simply follow the policy, which often states that a refund, replacement, or credit should be issued when the claim appears to meet the rules. Those rules were written for a world where proof was harder to fake, and few brands have a reliable way to tell when an image or story has been manipulated with AI.
Most retailers don’t have a consistent, cross‑team way to flag suspicious content and compare it to past behavior. Support teams see individual cases, often from what appear to be different customers. But in reality, the same person may be creating new profiles and repeating the same AI‑assisted claims, which is impossible to spot in a single ticket.
Rethink How You Respond to AI-Driven Return Fraud
The knee-jerk reaction for brands is to tighten return policies and train agents to say “no” more often. That feels like taking control, but it punishes loyal customers while determined abusers simply upgrade their AI tools and create more accounts to keep going.
Fraudsters will only find new and creative ways to use AI. Today, they’re generating fake photos, but tomorrow, they’ll use a new tactic. This is why retailers need to stop treating each claim in isolation and instead look for important patterns. How often does a customer report an issue? What kinds of issues do they report? How do their claims compare to those of other customers?
Use AI to Detect AI-Assisted Abuse
Retailers need the ability to connect orders, returns, claims, support tickets, credits, and even basic interaction patterns (like device and address history) into a single view of each customer. Then, flag behaviors that deserve a closer look, such as repeated “item not received” claims, frequent high‑value “damaged” reports, or clusters of accounts sharing the same details.
Update Your Policies Based on Behavior
Make policies more dynamic, rather than stricter across the board. Trusted customers, those with normal return behavior, can continue to enjoy fast, generous resolutions with minimal friction. Meanwhile, high‑risk customers can be routed into different flows, such as extra checks before issuing a refund, different return options, smaller credits, or, in some cases, blocked future claims.
Over time, that kind of behavioral playbook does more to protect both your margins and your best customers than another round of blanket crackdowns.
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Respond to AI Tactics With a Strong System of Defense
AI tactics in return fraud will continue to evolve, and they’re already forcing brands to confront how little they truly understand about post-purchase behaviors. But treating every customer like a suspect won’t fix retail’s returns fraud problem.
You can keep adding restrictions and hope your best customers tolerate the extra friction, or you can invest in technology that shows customer behavior end‑to‑end. Your team needs a defense system that can keep pace with these ever-evolving AI-driven return-fraud tactics. That way, claim resolution becomes less about the believability of a one-off story or supporting imagery and more about the identified patterns in how a customer shops, returns, and engages with support.