Topic starter
10/07/2026 9:21 pm
TL;DR: Gen AI has been used by 50% of consumers to draft return or refund claims, while 85% see at least some deceptive return behavior as normal, according to Riskified’s 2026 survey of 2,091 consumers across seven markets. The data shows return fraud is now a governance problem, not just a dispute-handling problem.
Editorial analysis by NHI Mgmt Group, based on content published by Riskified: “The new returns reality”.
Key questions
Q: What breaks when AI can draft return claims for consumers?
A: The main failure is that claim quality stops being a trustworthy fraud signal.
Q: Why do deceptive return tactics become harder to control once they are normalised?
A: Normalisation lowers the social cost of abuse and makes policy enforcement less effective because more customers see manipulation as acceptable.
Q: What signs show that a return programme is too dependent on customer-facing claims?
A: Common signs include heavy reliance on written explanations, inconsistent treatment of repeat abusers, and high manual-review effort with little reduction in losses.
Practitioner guidance
- Prioritise behavioural history over claim wording Weight purchase patterns, prior return behaviour, and customer lifetime value above the quality of the written claim when deciding whether to approve, inspect, or reject a return.
- Build tiered return workflows Route claims into low, medium, and high-risk paths so that obvious abuse can be rejected, ambiguous cases can carry disclosures, and low-risk claims can be accepted with tracking.
- Tighten evidence checks on manipulated returns Increase scrutiny of photos, damage descriptions, and inconsistent supporting material when the claim history or item profile suggests a higher abuse risk.
Bottom line: AI-assisted return claims reduce the reliability of claim quality as a fraud signal, so merchants need to lean on behavioural context instead of persuasive wording.
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10/07/2026 9:44 pm
AI-assisted return claims create a signal integrity problem, not just a volume problem. Once consumers can use gen AI to draft convincing narratives, the visual and linguistic polish of a claim stops being a reliable proxy for legitimacy. That forces merchants to elevate behavioural history, not presentation quality, as the deciding control. The practitioner conclusion is simple: fraud teams need signal hierarchies, not text-based intuition.
A question worth separating out:
Q: Should merchants tighten return policies for everyone or target risky customers only?
A: Targeted controls are usually safer. Blanket tightening can alienate honest customers and reduce trust, while selective friction lets merchants focus on customers with repeat abuse patterns, suspicious evidence, or high-loss claim types without making the entire return journey painful.
👉 Read our full editorial: AI-assisted return claims are reshaping ecommerce fraud controls