Return abuse turns identity data into the holiday control plane

At a glance

What this is: NHI Mgmt Group's analysis, drawing on Forter's blog post: Holiday return abuse is a customer identity problem, not just a refund problem, because order-level controls miss abuse that spans accounts, devices and sessions.

Why it matters: Identity-linked policy matters because fraud, operations and customer experience all absorb the cost when return abuse is judged only at the order or account level.

By the numbers:

• Forter data shows false INR claims are up 30% year-over-year.

Context

Holiday return abuse is the misuse of returns, refunds or item-not-received claims to keep merchandise and money at the same time. In ecommerce, the control failure usually starts when the merchant treats each order as a separate event instead of tying behaviour back to a persistent identity.

That problem becomes sharper during peak season because reseller fraud, wardrobing and false INR claims can all look legitimate in isolation. The operational lesson is that returns policy is really a customer governance problem, not a post-purchase workflow problem.

Key questions

Q: How do I detect false item-not-received claims?

A: Look for repeated INR claims across different accounts, shipping addresses and devices, especially when the same customer shows inconsistent behaviour over time. The strongest signal is not a single disputed order but a pattern of refunds, replacements and account reuse that points to the same actor behind multiple transactions.

Q: Why do order-level fraud rules miss return abuse?

A: Order-level fraud rules miss return abuse because the purchase may be legitimate while the refund, swap or wardrobing behaviour is fraudulent. Effective controls need identity-linked signals such as account history, device reuse, payment instrument linkage, shipping and return address patterns, and manual review for anomalous return frequency or item condition disputes.

Q: How should merchants balance strict returns policy and customer churn?

A: Merchants balance strict returns policy and customer churn by using risk-based returns controls, not blanket denial. Low-friction customers get standard refunds, while high-risk behaviour triggers proof-of-purchase checks, receipt validation, serial number matching, or manual review. Clear policy rules and consistent exception handling reduce abuse without punishing legitimate buyers.

Q: What is the best way to use identity data in refund decisions?

A: Use identity data in refund decisions as a risk signal, not as the sole basis for denial. Link the customer account, payment instrument, delivery address, device fingerprint, and return history to detect repeated abuse, then combine that with claims evidence, item verification, and human review for exceptions.

Technical breakdown

Why order-level fraud controls miss repeat return abuse

Order-level controls only evaluate the current transaction, so they struggle when abusive behaviour is split across multiple accounts, addresses or cards. In return abuse, the same person can look like many low-risk customers, which prevents simple threshold rules from spotting patterns such as repeated INR claims or split-shipment manipulation. The problem is not a lack of alerts. It is a lack of identity continuity across the customer lifecycle.

Practical implication: Move abuse detection from single-order logic to persistent customer identity scoring before refund decisions are made.

How identity-linked signals change refund and return decisions

Identity-linked decisioning combines signals from devices, sessions, accounts and behavioural history to build a fuller view of the customer. That allows merchants to distinguish a trusted buyer from a serial abuser even when the transaction itself looks normal. The technical shift is from isolated rule evaluation to risk-based orchestration that can apply different treatment paths to the same activity depending on the identity behind it.

Practical implication: Use multi-signal identity correlation to route trusted customers, borderline cases and clear abuse into different decision paths.

Where customer service workflows become a fraud control surface

Customer service chat, refund handling and manual review are part of the fraud surface because they can be steered by false claims if representatives lack context. When support staff see only the immediate complaint, they may approve refunds that should have been risk-scored first. This is especially relevant when package theft is also plausible, because the same complaint category can hide both genuine loss and intentional abuse. Read Customer Service AI Agents Security.

Practical implication: Give support teams identity risk context and refund guardrails so they do not act on isolated complaints alone.

Threat narrative

Attacker objective: The objective is to obtain merchandise or refunds while avoiding detection by appearing like separate legitimate customers.

1. Entry begins when a customer opens a legitimate support or chatbot interaction and presents a false item-not-received claim.
2. Credential_harvested is not the right pattern here, because the abuse relies on identity fragmentation rather than stolen credentials.
3. Escalation occurs when the same actor uses multiple accounts, addresses or identities to bypass limits and secure refunds or reshipments.
4. Impact is inventory loss, margin erosion and higher operational cost from refunds, shipping and manual review.

Breaches seen in the wild

• McHire default password flaw 2025: A forgotten test admin account with the password 123456 and an API flaw exposed McDonald's McHire applicant records to researchers.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Holiday return abuse is an identity governance problem disguised as customer service friction. The decisive failure is treating refund decisions as isolated order events rather than as part of a persistent customer identity model. When the same actor can spread activity across accounts, devices and sessions, the organisation loses the ability to tell legitimate dissatisfaction from organised abuse. The practitioner conclusion is that refund policy must be governed by identity, not by the queue that handles the complaint.

False INR claims expose a standing trust assumption that customer support can safely resolve risk after the fact. That assumption breaks down when the refund has already been issued and the goods have already left the fulfilment chain. By then, the merchant is trying to recover from a completed loss instead of preventing it. The practitioner conclusion is to move controls upstream into decisioning at the moment of claim handling.

Return abuse creates a cross-functional control gap because no single team owns the full loss path. Inventory, fulfilment, support and fraud teams each see a different slice of the behaviour, which makes serial abuse hard to prove and easier to repeat. A named concept emerges here: identity-split return abuse, where the same individual uses multiple personas to fragment risk signals. The practitioner conclusion is to unify evidence across the customer lifecycle before policy enforcement.

Seasonal abuse pressure turns policy design into a segmentation exercise. Blanket friction may reduce abuse, but it also drives churn when trusted customers are treated like fraudsters. The stronger model is conditional friction based on identity confidence, claim type and prior behaviour. The practitioner conclusion is to separate good-customer recovery paths from abuse escalation paths rather than forcing one rule for all returns.

From our research library:

• U.S. fraud losses are projected to reach $40 billion by 2027.

What this signals

Identity continuity is the missing control in return abuse prevention. Merchants that only assess an order at the moment of refund will keep missing repeat behaviour that is spread across alternate accounts and contact points. The control needs to shift toward customer-level scoring and lifecycle-wide evidence, because abuse becomes visible only when the whole pattern is linked.

Policy friction works best when it is selective. The mistake is to harden every return path in response to abuse spikes. That punishes legitimate buyers and can damage repeat purchase rates, while still leaving sophisticated abusers room to adapt. The better governance model is to tighten review where risk is elevated and keep trusted customers moving quickly.

For practitioners

• Define identity-based refund thresholds Apply different refund and replacement rules to trusted customers, borderline cases and known abuse patterns instead of using one threshold for every order.
• Correlate signals across accounts and devices Join sessions, device fingerprints, addresses and behavioural history so repeated abuse cannot hide behind multiple accounts or cards.
• Train support teams on claim triage Give customer service staff a clear escalation path for suspicious INR claims, wardrobing patterns and repeated high-value refund requests.
• Track leakage alongside conversion Measure refund leakage, abuse rates, complaints, repeat purchases and customer churn together so policy changes do not optimise one metric at the expense of another.

Key takeaways

• Holiday return abuse is most damaging when merchants treat the refund as an isolated event instead of a pattern across the customer lifecycle.
• The evidence points to repeated false claims, identity splitting and operational leakage that affect inventory, shipping and margin.
• The control gap is identity continuity, so the practical fix is to govern returns by person-level risk rather than by order alone.

Related NHI Mgmt Group resources: Refund and Returns Fraud Guide · Customer Service AI Agents Security · Online Fraud Regulation Map · Online Banking Security Guide

Key terms

• Reseller Fraud: Reseller fraud is abuse in which a person buys goods with intent to resell them for gain, often by using stolen, fake, or manipulated identities, payment methods, or accounts. It commonly appears in returns, promotions, and chargeback workflows, where the transaction looks legitimate but the underlying intent is fraudulent.
• False Item-Not-Received Claim: A false item-not-received claim is a refund or dispute request in which the customer says an order never arrived even though it did. It is a common form of ecommerce abuse because it can be difficult to prove quickly. Merchants typically need order, shipping, and identity signals to evaluate these claims accurately.
• Wardrobing: Wardrobing is the practice of buying an item, using it temporarily, and then returning it as if unused. It exploits generous return policies and is hard to detect when merchants rely on narrow checks instead of broader behavioural and purchase-pattern analysis.
• Identity Continuity: Identity continuity is the ability to preserve a workload’s verified identity across proxies, services, and other infrastructure boundaries. It matters because zero trust breaks down when a request loses its original proof of identity and falls back to network trust or header-based assumptions.

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, workload identity and secrets management in the context of practical access control. It is useful for practitioners who need a stronger model for linking identity, privilege and operational risk across systems.