How should retailers prepare for a surge in false item-not-received claims during peak holiday periods?

Retailers should tighten order monitoring before volume spikes, because false item-not-received claims often rise when fraud rings see more ecommerce activity. Fast shipping, strong carrier relationships, and delivery confirmation controls help reduce disputes. Teams should also watch for account patterns that suggest abuse, then route higher-risk orders into review or alternative delivery options before refunds are issued.

How False Item-Not-Received Fraud Surges During Peak Season

Peak holiday periods create a predictable fraud window because order volumes, carrier handoffs, and customer service load all rise at the same time. That combination makes it easier for fraudulent claims to blend into normal delivery noise. Retailers should treat false item-not-received claims as an operations and trust problem, not just a refund issue.

One useful planning signal is that 79% of organisations have experienced secrets leaks, with 77% of those incidents causing tangible damage. While that stat comes from identity security rather than retail claims, it reflects the broader pattern: once abuse becomes routine at scale, the impact is real and cumulative. The same logic applies to claims fraud, where small losses can compound quickly across a high-volume season.

Delivery confirmation matters most when the business can separate ordinary delay from deliberate abuse. A clean shipping record, carrier scan history, and visible handoff evidence help the team distinguish genuine non-delivery from claims that are opportunistic. When those signals are incomplete, the retailer is forced into a refund decision with weaker evidence than the claimant has.

Controls That Reduce Loss Before the Refund Decision

The strongest controls are the ones that make a false claim harder to file and easier to disprove. That means tightening order monitoring before the surge begins, applying stronger delivery confirmation for higher-risk shipments, and using carrier relationships to resolve scan exceptions quickly. If the customer or order pattern is suspicious, retailers should not wait until after a claim is opened to intervene.

Review should focus on repeatable abuse patterns rather than isolated friction. Multiple claims from the same account, address, device, payment profile, or delivery corridor can indicate organised fraud even if each individual order looks ordinary. The goal is not to block every delayed parcel, but to route the most abuse-prone orders into higher-friction handling before the merchandise leaves control.

Where available, retailers can also reduce exposure by offering alternative delivery options for risky orders, such as signature capture, pickup points, or carrier services with stronger proof of delivery. That approach preserves conversion for legitimate buyers while shrinking the refund surface for claims that would otherwise be difficult to contest.

Risk and Threat Considerations

False item-not-received claims are attractive because they exploit a weak evidence state, the retailer often has to prove delivery after the fact, while the claimant only needs to assert non-receipt. During peak periods, fraud rings can hide inside normal seasonal service issues, using volume and delay to make abuse look plausible.

Failure mechanism: Incomplete scan data, weak delivery confirmation, and delayed review allow bad claims to pass as routine customer disputes. Once the refund is issued, the loss is usually unrecoverable and the pattern can repeat across multiple orders or accounts.

Impact: The business absorbs direct refund and replacement cost, plus customer service burden, chargeback pressure, and erosion of trust in the returns process. At scale, the main risk is not a single fraudulent claim, but a claims workflow that becomes too easy to exploit when holiday traffic peaks.

Standards & Framework Alignment

This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.

OWASP Agentic AI Top 10 address the attack and risk surface, while CIS Controls v8 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.

Framework	Control / Reference	Relevance
CIS Controls v8	6 — Access Control Management	Controls risky order handling and claim approval paths through stronger access and review governance.
	8 — Audit Log Management	Delivery, account, and claim logs are essential evidence for distinguishing legitimate disputes from fraud.
Recommendation — Restrict refund approvals to authorized staff and require step-up review for high-risk claims. Centralize shipping and claims logs so investigators can reconstruct disputed delivery events quickly.
NIST CSF 2.0	PR.AC — Access Control	Supports limiting who can override claims and approve refunds during high-fraud periods.
	DE.CM — Continuous Monitoring	Order and account monitoring is needed to surface repeated abuse patterns before refunds are issued.
	RS.MI — Mitigation	The issue requires rapid containment of suspicious claims and repeat-abuse patterns.
Recommendation — Apply access controls to refund workflows and escalation paths to reduce opportunistic abuse. Monitor orders and customer patterns continuously to flag suspicious non-receipt claims early. Contain suspected claim abuse quickly by routing high-risk orders into manual review or alternative delivery.
OWASP Agentic AI Top 10	A1 — Goal Hijacking	Fraud rings can hijack customer-service workflows by pushing them toward undeserved refunds.
Recommendation — Constrain automated or semi-automated refund workflows so they cannot be steered into unsafe approvals.

Practitioner Guidance

What to prioritise: Build a pre-holiday review queue for orders that combine high value, shipment uncertainty, and repeat claim indicators. The best time to stop a false item-not-received claim is before the parcel enters the most ambiguous part of the delivery chain.

What to verify: Make sure the refund decision process can actually use carrier scan history, proof-of-delivery signals, account history, and order-device linkage in one place. If investigators must assemble evidence manually, the retailer will usually approve too many claims just to keep pace.

Common mistake: Treating every item-not-received report as a customer service exception. In peak season, that mindset rewards organised abuse because fraudsters depend on fast, low-friction resolution.

Practitioner takeaway: The practical goal is to move from reactive refund handling to evidence-based claim triage, with stronger controls on the orders most likely to be abused.