Item-not-received (INR) fraud has become the single most expensive form of policy abuse facing ecommerce merchants. When a customer falsely claims their order never arrived—then demands a refund, replacement, or chargeback while keeping the original product—the financial damage is immediate and compounding.

Not every INR claim is fraudulent. Packages get lost. Carriers make mistakes. Delivery failures happen. The challenge is telling the difference. When a false claim slips through, the costs add up fast: lost merchandise, shipping expenses, chargeback fees, and the staff hours spent investigating something that was never legitimate.

The broader impact extends beyond direct losses. False INR claims strain customer support teams, distort delivery performance data, and force merchants into an uncomfortable position: tighten policies and risk alienating honest customers, or absorb the losses and watch abuse escalate.

What is item-not-received fraud? 

INR fraud occurs when a customer falsely reports non-receipt of an order — also known as a false INR claim —  to secure a refund, replacement item, or chargeback—while keeping the original product. It’s a form of policy abuse that exploits the customer-friendly refund and return policies most merchants have built to reduce friction and build trust.

The mechanics are straightforward: a fraudster completes a legitimate purchase, receives the item, then contacts the merchant or their bank claiming it never arrived. In many cases, they’ll provide fabricated evidence—altered tracking screenshots, fake correspondence, or claims of porch theft—to support the story. This abuse can result in significant losses for merchants, including the cost of lost merchandise, shipping expenses, and chargeback fees. Additionally, it strains customer support resources, skews delivery performance metrics, and can erode trust in otherwise customer-friendly policies.

The financial impact of INR fraud 

According to Riskified’s Policy Benchmarks Report, 55% of merchants rank INR abuse as their most serious policy abuse concern—ahead of returns fraud, promo abuse, and account takeover. The same report found that 93% of merchants cite INR disputes as the primary driver of abuse-related write-offs, with INR claims accounting for as much as two-thirds of all abuse-related losses. Companies that ship products worldwide lack visibility into their last-mile delivery partners and therefore cannot confidently validate claims. Or, on the flip side, repeated or serial abusers get away as merchants lack more automation and data intelligence that is required for detecting fraudulent claims.

Across high-value sectors such as retail, electronics, and fashion, the volume of INR claims is substantial. Merchants’ inability to differentiate between good, paying customers and potential abusers makes this problem uniquely difficult to address. The threat is twofold: it involves fraudulent INR carried out by career fraudsters, but most importantly, it is also carried out by paying regular – and sometimes loyal – customers as well (often referred to as liar-buyer or friendly fraud).

Because these customers have genuine purchase histories and no obvious red flags, they’re significantly harder to identify and stop. Lenient refund thresholds and accommodating replacement policies—originally designed to attract and retain customers—have made it easy to abuse INR processes and equally difficult for merchants to push back without risking legitimate customer relationships.

Therefore, Simply targeting sophisticated fraud rings isn’t enough; merchants must also work to identify and reduce abusive patterns among all customers. The challenge is that merchants often lack the time or resources to follow up on claims and in reality, investigating individual INR claims can be more costly than simply accepting the customer’s claims. 

INR fraud prevention: Real merchant results

For many merchants, the barrier to addressing INR abuse isn’t awareness—it’s scale. Manual review processes simply cannot keep pace with the volume of claims, and without data intelligence, it’s nearly impossible to identify patterns across thousands of transactions.

One major US retailer confronted this directly. Relying on manual review, the team was able to reject only 0.8% of suspected INR claims—catching a fraction of the actual abuse and burning significant staff time in the process. By implementing Riskified’s network intelligence and shifting to identity-based, automated claim review, the retailer was able to:

• Prevent 10x more misuse than the previous manual process
• Increase the claim rejection rate for suspected cases from 0.8% to 8% 
• Generate an estimated $13.3 million in annual misuse prevention

The results illustrate a consistent truth: manual review at scale is not a viable long-term strategy. Automation and identity intelligence are what move the needle.

How to detect and prevent false INR claims?

Managing INR abuse at scale requires more than reactive policies—it demands proactive investment in the right tools, data, and processes. Here are the four most effective levers merchants can pull:

Invest in automation and identity intelligence.

Manual review cannot scale. Automated claim review tools—particularly those that use identity-based signals like device fingerprinting, behavioral history, and network-level patterns—can flag suspicious claims instantly and consistently. This frees your team to focus on edge cases and high-impact prevention measures ather than routine processing.

Increase data transparency and control visibility 

Provide teams with more detailed customer data, like purchase or return history, to better detect potential instances of fraud and abuse. A customer filing their fourth INR claim in six months is a very different risk profile from one filing their first. Purchase history, return behavior, claim frequency, and associated accounts are all signals that can dramatically improve detection accuracy—but only if they’re surfaced in the workflow where decisions are made.

Analyze and reinforce your policies

Conduct a regular audit of your refund and replacement policies and existing fraud rules with an eye toward abuse vectors. Where are the loopholes? Which policies are being exploited most frequently, and by whom? Tightening specific thresholds—for example, capping no-questions-asked refunds for high-value items or repeat claimants—can reduce abuse without meaningfully impacting the experience of honest customers.

Dedicate cross-functional resources to INR abuse prevention

INR fraud sits at the intersection of fraud, customer service, logistics, and finance—which means it often falls through the cracks organizationally. Dedicated resources, whether a specialized team or a coordinated cross-functional working group, ensure that abuse patterns are monitored continuously and that insights from one department (e.g., a spike in claims for a specific carrier) inform decisions in another.

The path forward

INR fraud is not a one-time issue and requires ongoing attention. As ecommerce evolves and fraud tactics grow more sophisticated, merchants must remain proactive and invest in systems to detect, prevent, and manage abuse at scale. 

Success will come to merchants who go beyond manual processes and guesswork, opting instead for solutions that integrate automation, identity intelligence, and cross-functional teamwork. The risks of inaction are significant, but the solution is clear.

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Ecommerce fraud: Types, trends, and prevention