Turning the Tables: Combating First-Party, Friendly and Refund Fraud

Over the last 12 months, businesses have witnessed a significant jump in refund fraud. This growing menace operates on a more subtle and insidious level, unlike conventional fraud types, which require stolen credit card details or sophisticated hacking tools. Fraudsters exploit the trust-based nature of customer service policies, manipulating return and refund processes without needing to access sensitive financial information. This makes refund fraud particularly difficult to detect and combat, as it often blends in seamlessly with legitimate customer complaints.

The critical challenge businesses face is differentiating between genuine customer grievances and fraudulent claims. Refund fraud often involves tactics such as “wardrobing,” where customers return used items as if they were new or falsely claim that an item never arrived or was damaged during shipping. According to the National Retail Federation, fraudulent returns accounted for 13.7 percent of all returns in 2023, resulting in estimated losses of around $101 billion. This puts businesses in a precarious position: they must maintain high customer service standards to satisfy their customers while protecting against increasingly sophisticated refund fraud schemes.

Balancing these two priorities is no easy task. Companies that are too lenient risk becoming targets for fraudsters, while those that are too stringent may alienate legitimate customers, leading to a loss of trust and potential revenue. As e-commerce continues to grow, with online return rates outpacing those of brick-and-mortar stores, the threat of refund fraud is only expected to increase. The imperative for businesses is clear: they must develop more nuanced and practical strategies to differentiate between legitimate and fraudulent refund requests, ensuring they protect their bottom line and customer relationships.

The Many Faces of Refund Fraud

Each type of refund fraud has unique challenges and implications. First-party fraud occurs when customers intentionally abuse return policies to secure financial gain. These individuals often exploit loopholes in a company’s return policy, knowing that customer service teams generally tend to side with the customer to maintain satisfaction and brand loyalty.

Friendly fraud is another common type, where customers either mistakenly or dishonestly request refunds for legitimate purchases. Unlike first-party fraud, which is premeditated, friendly fraud can sometimes stem from genuine confusion — such as a customer forgetting they made a purchase — or from opportunistic behavior, where customers falsely claim they never received an item or that the product wasn't as described. This type of fraud is particularly tricky to manage as it often involves customers who may not see themselves as fraudsters but are nonetheless exploiting the system.

Finally, refund fraud involves more calculated efforts to exploit e-commerce systems. Fraudsters may, for example, order products and then claim they never arrived, even going as far as to increase their profits by reselling items on secondary markets like eBay and then seeking refunds from the original sellers. These schemes can be sophisticated, involving multiple accounts, fake identities and other deceptive practices. Understanding these distinctions is crucial for businesses aiming to develop targeted strategies to effectively combat each type of fraud.

A Deeper Look at Refund Fraud Tactics

To effectively combat refund fraud, businesses need first to understand and analyze the tactics employed by fraudsters. One of the most critical steps in this process is account history analysis. By scrutinizing the ratio of problematic orders to total orders, businesses can identify patterns that may indicate fraudulent behavior. For instance, if a new account places an order almost immediately after registration, this could be a red flag, especially if the account’s subsequent activity shows a high frequency of returns or refund requests. Understanding the timeline between account creation, order placement and overall order history can provide invaluable insights into whether a refund request is legitimate or part of a broader scheme.

Another vital aspect of fraud detection is spotting repeat offenders. Fraudsters often try to scale their operations by creating multiple accounts, each with a different identity but similar behavioral patterns. They may use prepaid, gift or virtual cards, which are easier to obtain and less traceable than traditional bank cards. Monitoring bank identification numbers (BIN) associated with these transactions can help businesses adjust risk levels and identify potential fraud. Additionally, tracking the use of these cards across multiple accounts can reveal patterns that single-account analysis might miss.

Email and phone inspection is another layer of defense against refund fraud. Fraudsters frequently use newly created email addresses and phone numbers with little or no digital footprint to avoid detection. These identifiers are often temporary and used for the sole purpose of executing a fraud scheme. Businesses can take preemptive action by flagging accounts with suspicious email and phone number activity, such as requiring additional verification before processing returns or refunds. Similarly, device and IP analysis play a crucial role. Fraudsters may use the same device or browser across multiple accounts or deploy emulators and fraud-specific browsers to mask their activities. Additionally, using proxies to hide IP addresses can indicate fraudulent intent. Tracking these technical details helps businesses identify repeat offenders and prevent them from exploiting their systems.

The Retail Industry’s Response to Refund Fraud

Preventing return fraud requires a proactive approach, as once the fraud has occurred options for recourse are limited. Confronting the buyer, particularly if they're experienced fraudsters, is often unproductive. Instead, businesses need to focus on prevention strategies that can stop fraud before it happens. For instance, some online marketplaces like eBay provide tools to block high-risk buyers, such as those with unpaid item strikes or negative feedback. On platforms like Amazon.com, where seller protection is less robust, relying on manual monitoring of return patterns or the platform’s built-in fraud detection capabilities becomes essential.

An effective way to prevent return fraud is by requiring more stringent identification and contact information for returns. Instead of only asking for a receipt, especially for online purchases, retailers should verify the buyer’s contact details and cross-reference them with the original order. A risk management process in place, where potentially flagged orders require additional checks (e.g., SMS verification) can further reduce the likelihood of refunding items purchased with stolen cards.

Eliminating cash refunds and offering store credit or gift receipts can also disincentivize fraudsters, who are often more interested in quickly liquidating the return value rather than obtaining the goods themselves. Updating return policies to include specific measures tailored to your products can also be a strong deterrent against fraud. For example, weighing electronic items upon return and comparing them to their original weight can prevent fraud schemes like “bricking,” where valuable components are removed from a product before it is returned.

Analyzing buyers’ digital footprints has become an increasingly accessible and effective online business strategy. By examining digital traces like social and digital profiles connected to a user’s email address or phone number, IP data, device information and browsing behaviors, businesses can detect inconsistencies that may signal fraudulent intentions. This innovative technology, alongside machine learning and behavioral analytics, can be particularly effective in analyzing vast amounts of transaction data and identifying unusual patterns and destructive behaviors. As these systems continuously learn from new data, they become more adept at catching fraudulent activities in real time, making it increasingly difficult for fraudsters to succeed. Coupled with historical data analysis, which helps identify common fraud patterns and repeat offenders, businesses can develop more refined and effective fraud prevention strategies, staying ahead of the evolving tactics used by fraudsters.

Tech is Your Friend

Leveraging advanced technology is no longer a luxury but a necessity to combat return fraud. Fraud detection systems offer powerful tools that help businesses proactively identify and mitigate fraudulent activities before they escalate. By collecting and analyzing comprehensive data points — e.g., device information, email addresses, phone numbers and IP addresses — retailers can better detect anomalies and demonstrate the intent behind transactions. These enriched data sets enable businesses to differentiate between legitimate customers and potential fraudsters, allowing for more targeted responses.

Automated technology systems can also push disputes directly to card issuers and networks, streamlining the process of challenging chargebacks. This proactive approach ensures that businesses respond swiftly to fraudulent claims, often recovering lost revenue and deterring future attempts. Combined with continuous monitoring and customer profiling, these technologies provide a more robust defense against first-party fraud, enabling businesses to act before fulfillment and reduce the risk of fraudulent returns.

As technology evolves, so must the strategies employed to combat fraud. Machine learning and behavioral analytics are becoming increasingly vital in processing vast transaction data and identifying subtle patterns that human analysis might miss. These systems are not static; they continuously learn and adapt, becoming more effective at catching fraudulent activities in real time. By coupling these advanced technologies with historical data analysis, businesses can stay ahead of fraudsters, refining their fraud prevention strategies to protect their revenue and customer relationships.

Matt DeLauro is chief revenue officer at SEON, a provider of fraud prevention tools.