Serial Returners and Refund Fraudsters: How Brands are Fighting Back With Data Insight

Online return fraud cost U.S. retailers $22.8 billion in 2022. For every $100 in returned merchandise accepted, U.S. retailers lose $10.40 to return fraud, the NRF has calculated.

While it is possible for shoppers to commit return fraud innocently simply by mis-reading the returns policy, a significant number of returns are the result of premeditation and malicious intent.

‘Serial returners’ are shoppers who deliberately buy several items knowing full well they won’t keep some of them. ‘Wardrobers’ abuse the system by wearing clothes for one occasion only, then sending them back. More overt scams include claiming that an item that arrived has never arrived, or that the item does not match the product description or was damaged in transit. Signifyd data shows that there was a 35% increase in false claims that an item never arrived in 2022 in Europe, and a 68% increase in false claims about the condition of a product. U.S. retailers are likely to be experiencing similar trends.

Other fraudulent activities include returning shoplifted items, and ‘merchandise exchange’ whereby shoppers purchase a new item, then return an older or non-working version of the same item using the packaging from the newer merchandise.


Return fraud scams are not solely practiced by individuals. Large, multinational crime rings may be behind an attack.

Cutting Fraud to Claw Back Costs

Retailers are pushing back. For instance, in 2019, Asos said it would begin deactivating the accounts of serial returners on its site. Some fashion brands, including Zara, J.Crew and Uniqlo, have begun charging for returns in order to claw back lost revenue and dissuade certain behaviors.

Above and beyond these measures, the key to addressing the scale of these problems is having digital systems in place that will alert retailers and spot which SKUs are problematic, and which customers are causing a hit to profits. It’s also useful to ascertain where in the world profit-draining activity is taking place. For retailers with overseas customers, the returns process can be more complex, with potentially more loopholes for returns fraud.

Data analytics of returns patterns is something Asendia can offer clients through the e-PAQ Returns solution. Our system provides a full suite of retailer reports and dashboards. The power is in spotting the trends and taking timely action to stamp out returns fraud activity, wherever in the world it is happening.

Naturally, retailers can also use their CRM databases and customer transaction data to investigate suspected fraudulent actions.

Here are five ways data analytics can assist in the fight against returns fraud:

Pattern recognition and anomaly detection: Data analytics can analyze vast amounts of transaction, inventory and customer data to establish normal shopping patterns and behaviors. By using advanced algorithms, retailers can identify anomalies that might indicate returns fraud. Unusual patterns, such as frequent returns of high-value items, or returns from the same address with different customer names, can be flagged for further investigation. Analysis of the reason for returning can also flag suspicious, repetitive behavior.

Customer data analysis: This can be carried out to detect potential networks of fraudsters working together. By examining shared IP addresses, shipping addresses and payment methods, retailers can uncover hidden associations that might indicate organized fraud rings. Identifying such networks can help retailers take pre-emptive action to prevent fraud.

Predictive modeling for fraud prevention: Leveraging historical data, data analytics can build predictive models that assess the likelihood of a particular transaction being fraudulent. These models can factor in various variables, such as purchase history, device information and behavioral patterns. Retailers can then set customizable risk thresholds, automatically flagging or blocking transactions that exceed these thresholds, thereby reducing returns fraud.

Real-time monitoring and alerts: Implementing real-time data analytics allows retailers to monitor transactions as they happen. By using machine learning algorithms, retailers can instantly evaluate each purchase for signs of potential fraud. If suspicious activity is detected, alerts can be sent to fraud prevention teams for immediate action, such as manual review or order cancellation.

Customer behavior analysis: Data analytics can help retailers gain insights into individual customer behaviors, enabling them to distinguish between legitimate returns and fraudulent ones. By analyzing historical transaction data, product preferences and return patterns, retailers can identify customers who consistently exploit return policies. Retailers can then implement targeted measures, such as limited return options or increased scrutiny, for these individuals.

Final Note: Enforce Your Returns Policy

Having a clear and stringent returns policy — and enforcing it — should protect retailers from a great deal of fraudulent returns.

Well-established parcel shipping partners, who are increasingly handling returns on behalf of retailers, are in a strong position to advise on exactly what’s needed, and to keep their retail clients up to speed on the latest scams.

Data analytics does offer online retailers a powerful arsenal against returns fraud by detecting unusual patterns and enabling informed decision-making. By leveraging these capabilities, I believe online retailers can significantly reduce returns fraud. Further, implementing the appropriate returns platform not only amplifies operational efficiency, it also protects the bottom line, while fostering a trustworthy shopping experience for regular, honest customers.

Helen Scurfield, an adept leader, drives global ecommerce solutions as Asendia UK’s Innovation and Development Director. With extensive experience spanning 20+ years in international logistics, including key roles at DPDgroup, GeoPost, and Royal Mail, she excels in strategic customer solutions, product development and operational enhancement. She efficiently integrates new carriers, enhances data quality and oversees warehouse automation. Scurfield’s expertise aids retail giants like Urban Outfitters and through cutting-edge parcel shipping and ecommerce fulfillment. Her team, collaborating with Asendia’s IT unit, has also conquered challenges like Brexit, COVID-19 and now new EU VAT rules. Scurfield’s journey from Royal Mail’s accountancy training scheme to finance and then to project management and IT in Hong Kong showcases her adaptability and innovation prowess.

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