Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by Riskified: “Return Abuse Goes Mainstream as Nearly Half of Consumers Use AI in Return Claims, New Riskified Research Finds”.
By the numbers:
- Nearly half of consumers (50%) report using generative AI tools such as ChatGPT or Claude to help draft return or refund claims.
- Overall, 85% of consumers accept at least one type of strategic or borderline return behavior.
Key questions
Q: What should fraud teams do first when return abuse starts to scale with AI-assisted claims?
A: Start by segmenting return flows by customer risk instead of sending every suspicious claim to manual review.
Q: Why does AI-assisted return abuse create risk for ecommerce policy governance?
A: Because it reduces the cost of producing convincing claims while leaving merchants with the same policy logic.
Q: What are the signs that return controls are too blunt for current abuse patterns?
A: High manual review volumes, inconsistent outcomes across similar customers, rising disputes over policy exceptions, and repeated abuse across different identities or payment methods are all warning signs.
Practitioner guidance
- Define risk-based return tiers Map return windows, reason codes, and refund paths to customer risk segments so trusted shoppers are not forced through the same controls as repeat abusers.
- Incorporate identity signals into review Use device, payment, and historical behaviour signals to separate legitimate return claims from coordinated or repeated abuse patterns.
- Reduce blanket manual review Reserve human review for exceptions that show cross-identity repetition, unusual claim language, or policy edge cases that automated rules cannot resolve.
Bottom line: AI-assisted return abuse is making ecommerce governance more identity-driven, because the same claim can carry very different risk depending on customer behaviour and history.
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14/07/2026 4:29 pm
AI-assisted return abuse is an identity governance problem disguised as customer service. The article shows that the main challenge is not simply detecting fraud language, but deciding who should receive friction, when, and under what policy. That moves returns into the same governance territory as risk-based access decisions: context matters more than a single request. Merchants that treat every return as equivalent will misclassify both loyal and abusive behaviour.
A few things that frame the scale:
- Organisations that describe themselves as confident in their AI deployment actually experience a 72% security incident rate, compared to 33% for those who remain cautious, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should merchants use personalised return policies or uniform rules?
A: Personalised or tiered policies are usually more defensible when abuse is unevenly distributed across customer groups. Uniform rules can protect simplicity, but they often over-penalise good customers and still fail to stop repeat abusers. The right model depends on whether the merchant can support identity-aware segmentation at scale.
👉 Read our full editorial: AI-assisted return abuse is reshaping ecommerce risk governance