Topic starter
11/11/2026 6:05 pm
TL;DR: Holiday return abuse now spans false INR claims, wardrobing and reseller fraud, according to Forter, and the data point to a 30% year-over-year rise in false claims. Merchants need identity-linked controls because order-by-order checks cannot see abuse spread across accounts, devices and sessions.
Editorial analysis by NHI Mgmt Group, based on content published by Forter: “How to Stop Holiday Return Abuse and Reseller Fraud”.
By the numbers:
- Forter data shows false INR claims are up 30% year-over-year.
Key questions
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.
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.
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.
Practitioner guidance
- 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.
Bottom line: Holiday return abuse is most damaging when merchants treat the refund as an isolated event instead of a pattern across the customer lifecycle.
Glossary terms: Reseller Fraud, False Item-Not-Received Claim, Wardrobing, Identity Continuity
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11/11/2026 6:08 pm
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.
A few things that frame the scale:
- U.S. fraud losses are projected to reach $40 billion by 2027.
A question worth separating out:
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.
👉 Read our full editorial: Return abuse turns identity data into the holiday control plane