E-Commerce · Refund & Return Fraud

Visual Fraud Prevention

Generative AI is being used to add fake damage to product photos and submit them as refund evidence. Catch it before you issue the refund.

The Return Score reads the refund claim text, identifies what damage was described, and focuses the forensic scan on exactly those image regions.

Fashion & Apparel Food Delivery Furniture & Home Consumer Electronics
Request access View API docs →
Woman holding jeans with damage
Photo by Freepik
Return Fraud Score
100 = fully AI-generated · 0 = authentic
0 / 100
0-50 50-70 70-80 80-100
Recommended action Require return / reject
Context Engine
1 Reading refund claim
"Received the jeans with a hole in the back pocket area..."
2 Extracting damage claims
hole back
3 Directing analysis
Focusing forensic scan on rear pocket region...
4 Region score
Inpaint boundary: 91% · Texture mismatch: 87% · Denim weave absent: 94%

How refund fraud works

Refund-without-return policies save millions in reverse logistics. But they also create an attack surface: submit a convincing photo of damage, and the refund is issued automatically.

01
Customer receives item
Product arrives in perfect condition. Customer opens the package and inspects the item.
02
Damage added in post
Using generative AI tools, the customer inpaints realistic-looking damage onto a photo of the product.
03
Fraudulent claim submitted
"Item arrived with burn hole in front. Requesting full refund."
04
Manual review can't keep up
Human reviewers process thousands of claims daily. AI-generated damage is visually indistinguishable at scale.

Real damage vs. AI-generated damage

"Received the sweater with a burn hole in the front panel. The fabric is visibly singed and damaged."

Real t-shirt with damage
Scan Region
AI-generated image
Claim text: "Received the sweater with a burn hole in the front panel. The fabric is visibly singed and damaged."
Damage is authentic
Fiber distortion
92%
Char gradient
89%
Texture continuity
94%
Noise signature
88%
12 / 100 Return Score
AI-generated t-shirt with fake damage
Anomaly
AI-generated image
Claim text: "Received the sweater with a burn hole in the front panel. The fabric is visibly singed and damaged."
AI-inpainted · fraud detected
Fiber distortion
92% anomaly
Char gradient
89% anomaly
Texture continuity
91% anomaly
Noise signature
87% anomaly
91 / 100 Return Score

Your rules. Our intelligence.

You define the thresholds. We provide the score. Automate refund decisions or route claims to the right team based on risk level.

0-50
Instant refund

Damage is consistent with the claim. Approve the refund automatically.

50-70
Request additional evidence

Some signals are ambiguous. Ask the customer for more photos or details.

70-80
Manual review

Multiple forensic signals triggered. Route to a human reviewer for inspection.

80-100
Require physical return or reject

High confidence of AI manipulation. Require the item to be returned before issuing any refund.

0%
Increase in AI refund fraud (2023-2025)
0%
Of e-commerce returns are fraudulent
+0%
Refund fraud increase year-over-year
0%
Of retailers report photo-based fraud

Know your refund fraud exposure

Run your refund claims through our forensic engine. See what percentage of damage photos contain AI manipulation.

Get in touch View API docs →