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14/07/2026 4:21 pm
TL;DR: A commissioned study of 2,091 consumers across seven countries found that 50% use generative AI to help draft return or refund claims, while 85% accept at least one strategic return behavior and 56% prefer tiered policies, according to Riskified research. The governance challenge is no longer just fraud detection; it is identity-aware policy enforcement at scale.
NHIMG editorial — based on content published by Riskified: Rewriting the Rules on Returns, which examines how AI and abuse are reshaping ecommerce returns
By the numbers:
- 50% report using generative AI tools such as ChatGPT or Claude to help draft return or refund claims.
- 85% of consumers accept at least one type of strategic or borderline return behavior.
- 56% also prefer personalized or tiered return policies rather than uniform approaches.
Questions worth separating out
Q: How should ecommerce teams handle AI-generated return claims without overblocking good customers?
A: Treat AI-generated claims as a reason to strengthen evidence, not to deny every request.
Q: Why do tiered return policies depend on identity confidence?
A: Tiered policies only work when the business can reliably recognise the same customer across accounts, devices, and payment methods.
Q: What do retailers get wrong about refund abuse controls?
A: Many retailers focus on blanket refund rules instead of claim-specific evidence and operational context.
Practitioner guidance
- Build identity-linked return scoring Correlate claims with device fingerprints, payment instruments, prior refund outcomes, and account history so abusive patterns are visible across channels.
- Separate legitimate persuasion from abuse signals Use human review only for cases where identity-linked signals and transaction context disagree, rather than for every well-written claim.
- Introduce policy tiers with governance thresholds Define when a customer can move into a faster or more flexible returns path, and require evidence thresholds before granting exceptions.
What's in the full report
Riskified's full report covers the operational detail this post intentionally leaves for the source:
- Consumer survey breakdown by country, age group, and return behaviour type for benchmarking policy design.
- Retail leader interview findings on how AI is affecting manual review, policy enforcement, and customer friction.
- Examples of differentiated return treatment based on customer risk and behaviour, useful for operational teams.
- Reported business outcomes from a luxury fashion brand using identity-based intelligence to reduce chargebacks and rejected-return losses.
👉 Read Riskified's report on AI-assisted return abuse and ecommerce policy risk →
AI-assisted return abuse: what it means for fraud and identity teams?
Explore further
14/07/2026 4:29 pm
AI-assisted return abuse is an identity problem disguised as a fraud problem. When consumers can use LLMs to generate convincing claims, merchants lose the old benefit of spotting poor language or obvious fraud cues. The real control question becomes whether the organisation can link behaviour across identities, devices, and payment methods with enough confidence to apply policy fairly. Practitioners should treat return governance as a customer identity and trust decision, not only a fraud review workflow.
A question worth separating out:
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. Useful signals include repeat-offender suppression, stable approval rates for low-risk customers, and reduced refund leakage on high-risk items. If friction rises everywhere, the controls are too blunt.
👉 Read our full editorial: AI-assisted return abuse is reshaping ecommerce risk governance
