Claimscan vs Signifyd
| Claimscan | Signifyd | |
|---|---|---|
| Primary problem | Fabricated, edited or AI-generated return/damage photos | Fraudulent orders and payment chargebacks |
| Stage of the lifecycle | Post-purchase, at the return/refund claim | At checkout, when the order is placed |
| Core method | Layered image forensics (metadata, provenance, pixel, reuse, AI-detection) | Order-risk scoring with a chargeback guarantee |
| Integration effort | Drag-and-drop or API; no warehouse or checkout changes | Platform/checkout integration |
| Output | Soft manipulation-likelihood verdict for human review | Accept/decline order decision, financially guaranteed |
| Best for | SMB/D2C sellers refunding on photos alone | Merchants with significant card-fraud exposure |
Which do you need?
If your losses come from fraudulent purchases and the chargebacks that follow, a transaction-fraud platform such as Signifyd targets that directly. If your losses come from customers submitting manipulated or AI-generated photos to claim refunds or replacements after a legitimate purchase, that is the gap Claimscan is built for.
Running both is common: checkout fraud prevention does not inspect a post-purchase damage photo, and photo forensics does not score an order at checkout.
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Frequently asked questions
Is Claimscan a Signifyd competitor?
Only loosely. They tackle different stages — Signifyd at checkout, Claimscan at the return claim — so many merchants use them side by side rather than choosing one.
Does Signifyd detect fake return photos?
Signifyd's focus is order risk and chargeback protection at purchase. Forensic verification of a post-purchase claim photo is a different task, which is Claimscan's specialisation.
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