AI-assisted return claims: what fraud teams need to change

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.

NHIMG editorial — based on content published by Riskified: Rewriting the rules on returns

By the numbers:

• 50% of consumers have used gen AI tools to help draft return or refund claims.
• 85% of consumers surveyed see at least some form of strategic or deceptive return behavior as normal.
• 51% of global consumers have encountered return-related content on social platforms.

Questions worth separating out

Q: What breaks when return claims can be written by AI?

A: What breaks first is the assumption that polished wording or emotional detail correlates with honesty.

Q: Why do AI-assisted return claims complicate fraud detection?

A: They complicate fraud detection because they improve the quality and consistency of deceptive claims without changing the underlying pattern of abuse.

Q: How do merchants know if return controls are actually working?

A: They should look for lower abuse rates without a matching rise in false positives, customer complaints, or abandonment from legitimate shoppers.

Practitioner guidance

• Shift return screening to behavioural identity signals Weight account age, prior return rate, payment consistency, device continuity, and shipping history more heavily than claim wording or tone.
• Apply selective friction by risk tier Use tiered policies so high-risk claims trigger rejection or verification, medium-risk claims get added disclosures, and low-risk claims are accepted with monitoring.
• Tighten evidence requirements for high-value returns Require stronger proof for expensive items, unusual damage claims, or customers with a pattern of prior abuse, while leaving ordinary shoppers on the fast path.

What's in the full report

Riskified's full analysis covers the operational detail this post intentionally leaves for the source:

• The survey methodology across 2,091 consumers in seven markets and the senior retail leader interview set.
• The merchant decision patterns behind narrowing return reasons, shortening windows, and shifting toward store credit.
• The forum-reported examples of manipulated damage photos and step-based return screening policies.
• The regional differences in consumer exposure to return-related social content and how that shapes abuse risk.

👉 Read Riskified’s 2026 report on AI-assisted return claims and fraud control →

AI-assisted return claims: what fraud teams need to change?

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