AI-assisted return abuse is reshaping ecommerce risk governance

At a glance

What this is: NHI Mgmt Group's analysis, drawing on Riskified's research: Generative AI is normalising return abuse, with half of consumers using AI to draft claims and most accepting at least one strategic return behaviour.

Why it matters: Fraud, identity, and customer experience teams now need controls that distinguish legitimate shoppers from policy abusers without turning every return into a manual exception.

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.

Context

Return abuse is no longer confined to a small set of repeat offenders or obvious policy fraud. When consumers use generative AI to write persuasive claims, the governance problem shifts from simple claim validation to distinguishing intent, customer context, and policy boundaries at scale.

For fraud, identity, and ecommerce teams, the practical issue is that return decisions increasingly depend on behavioural signals rather than a single transaction. That makes policy design, customer segmentation, and review workflows part of the control surface, not just the fraud stack.

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. The aim is to reserve investigation capacity for repeat patterns, cross-identity behaviour, and policy edge cases. Without that triage layer, AI-assisted claims overwhelm reviewers and make both false positives and missed abuse more likely.

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. When language becomes cheap to generate, the control failure shifts to whether the organisation can evaluate customer history, identity context, and behavioural patterns before deciding on refunds or exchanges.

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. They show that the policy is treating very different risk levels as if they were the same case.

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.

Technical breakdown

Why AI changes return claim abuse

Generative AI lowers the effort required to produce credible, polished refund and return claims. That does not create fraud by itself, but it increases the volume and quality of borderline requests that teams must assess. In practice, AI acts as an amplification layer for behaviours that already sit near policy boundaries, such as bracketing, wear-and-return, or claims based on product mismatch. Traditional manual review is too slow and too inconsistent to absorb that change, especially when the same language patterns can be mass-produced across accounts.

Practical implication: Treat AI-assisted claims as a scaling problem for policy enforcement, not only as a content problem for fraud review.

Identity-based intelligence in post-purchase decisions

Identity-based intelligence in this context means using customer history, device signals, payment behaviour, and cross-order patterns to decide whether a return is likely legitimate or abusive. The article shows why this matters: the same consumer-facing policy can produce very different outcomes depending on whether the claimant is loyal, opportunistic, or repeatedly abusive. Without identity context, merchants are forced into uniform treatment, which either over-blocks good customers or under-controls bad ones.

Practical implication: Anchor return controls to customer risk and behaviour signals rather than relying on one-size-fits-all rules.

Tiered return policy as a control model

Tiered return policy is a governance pattern, not just a customer experience tactic. It allows merchants to shorten return windows, narrow reason codes, offer store credit, or add inspection when risk is elevated, while preserving fast paths for trusted customers. The security value is that controls move from blanket enforcement to proportional enforcement. That reduces friction where risk is low and concentrates scrutiny where abuse patterns are more likely to appear.

Practical implication: Design return policy tiers so high-risk behaviour receives stronger controls without degrading low-risk customer journeys.

NHI Mgmt Group analysis

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.

Identity-based intelligence is becoming the decisive control plane for ecommerce returns. The reported use of multiple identities, devices, and payment methods shows why transaction-level screening is too shallow. The named concept here is return policy segmentation debt, where uniform policy rules accumulate risk because they cannot reflect customer history or behavioural context. Practitioners should see segmentation as a governance requirement, not a marketing preference.

Generative AI is widening the gap between policy intent and policy execution. Consumers can now produce claims that sound compliant even when the underlying behaviour is not. That means enforcement quality depends on linking policy logic to behavioural evidence, not on the wording of the claim itself. For fraud and identity teams, the real question is whether controls can still separate trust from abuse once language becomes cheap to generate.

Retailers will increasingly need differentiated trust models across the customer lifecycle. The article points toward a future where high-value customers receive lower-friction handling while riskier patterns trigger tighter inspection and shorter windows. That is the same governance trade-off seen in mature IAM and PAM programmes: not every subject deserves identical treatment. Practitioners should expect board-level pressure to justify both customer experience and loss prevention with the same control model.

The market signal is clear: return governance is becoming a discipline of continuous risk evaluation. Static policy rules are too blunt for AI-shaped claims and heterogeneous customer behaviour. The strongest programmes will combine detection, identity context, and adaptive treatment into a single operating model. For practitioners, that means moving beyond fraud flags toward measurable policy outcomes.

From our research library:

• 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.
• Read next: Agentic AI Identity Risk Board Briefing

What this signals

Return policy segmentation debt: uniform return rules become harder to defend as AI makes abusive claims cheaper to produce and easier to disguise. The control problem shifts from deciding whether a claim is authentic to deciding which customer should receive which friction level, and that is a governance problem as much as a fraud one.

Retailers should expect more pressure to connect identity signals, behavioural history, and post-purchase policy in one decision layer. That does not mean every return needs more scrutiny; it means the scrutiny has to be targeted where abuse patterns are actually concentrated.

For practitioners

• 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.
• Align customer experience with loss controls Test whether stricter return terms still preserve low-friction treatment for high-value customers, because uniform restriction often shifts cost without improving control.

Key takeaways

• 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.
• Riskified's study shows that consumer acceptance of strategic return behaviour is already broad, which increases the scale of the control problem.
• Merchants need segmented return controls, not uniform friction, if they want to protect margin without damaging legitimate customer experience.

Key terms

• Identity-Relevant Intelligence: External information that helps an organisation understand how access, authentication, entitlement, or lifecycle failures appear in practice. In mature programmes, this intelligence supports review, tuning, and incident learning, but it never replaces internal telemetry or governance ownership.
• Tiered Return Policy: A tiered return policy applies different return windows, refund methods, or review levels based on customer risk and history. It is a governance model that balances customer experience with loss prevention by making treatment proportional to observed behaviour.
• Return Policy Segmentation Debt: Return policy segmentation debt is the accumulation of risk created when a merchant keeps using uniform return rules despite clear differences in customer behaviour. Over time, the policy becomes too blunt to protect margin without harming legitimate shoppers.
• Strategic Return Behaviour: Strategic return behaviour is a customer action that sits near or across a merchant’s acceptable-use boundary, such as bracketing, wearing before return, or exploiting policy gaps. The behaviour may be common, but that does not make it low risk for merchants.

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