What is return fraud and how to prevent it

E-commerce has become indispensable to retail frameworks around the world, providing customers and businesses with a flexible shopping experience that can save both time and money. However, high return rates are becoming an increasing challenge, creating additional costs and complexities for merchants. Fraudulent returns, unsurprisingly, incur even greater costs. 

While many cases of return fraud are carried out by lone actors, according to the National Retail Federation (NRF), organized retail crime (ORC) is a burgeoning threat within the retail industry. With such collaborative forces at work, compliance staff need to be aware of the red flag indicators of return fraud and how it can best be prevented.

What is return fraud?

Return fraud is a type of payment fraud that abuses a merchant’s return policy. It involves returning an item to a retailer that does not qualify for a return or refund, such as:

• Stolen merchandise
• Items that have already been used
• Items purchased from a different retailer
• Returning counterfeit items

Also known as return abuse, return fraud is regarded as one of the most common retail fraud typologies and can take place both online and in-store. 

What is the difference between return and refund fraud?

While return fraud centers around taking advantage of customer-friendly return policies, refund fraud involves making false claims about an item to receive a refund without returning the item in question. 

The revenue losses for the two different fraud types also vary. With return fraud, merchants lose the revenue from the initial sale, but sellers dealing with refund fraud also lose the revenue from any potential resale. 

What is the impact of return fraud?

While honest mistakes do happen, according to the NRF, “retailers incur $166 million in merchandise returns for every $1 billion in sales” – and lose $10.40 to return fraud for every $100 of returned merchandise accepted. This equates to an estimated $24 billion in losses per year.

Incidents of return fraud are particularly high during holiday seasons: 25 percent of annual product returns occur between Thanksgiving and New Year’s Day. According to credit reporting agency TransUnion, e-commerce fraud attempt rates between Thanksgiving and Cyber Monday in 2022 were 82 percent higher globally than the rest of the year.

Not only is return fraud a costly problem for businesses, it can also put customers at risk and damage an organization’s reputation. If a business tightens its policy to crack down on fraudulent activity, legitimate customers may become wary of making purchases if they believe their return may not be accepted. This can result in fewer sales and a loss of trust in the brand. 

What are the types of return fraud?

One of the reasons return fraud can be difficult to detect is that fraudsters employ numerous tactics to carry out their schemes. Some of the most common return fraud types include:

• Empty box scams: When fraudster customers falsely claim they have received an empty box instead of the intended merchandise and claim a refund. This fraud type can also refer to dishonest sellers who deliberately ship out empty boxes only to claim that it is the buyer’s word against theirs.
• Wardrobing: When consumers buy items, use them once, and return them later. This common type of return fraud has caused contention in the past, with many consumers believing it to be a harmless action. 
• Price switching: This type of scam refers to consumers that buy an item at one price before switching the price tag with a more expensive item and returning it for a refund. This fraud type is most prevalent in physical stores.
• Opportunistic: This type of return fraud occurs when consumers choose – either deliberately or unwittingly – the wrong reason for a return on a form. This isn’t necessarily a pre-meditated fraud type as many consumers are unaware that choosing the incorrect “reason” will affect the merchant.
• Bricking: This type of return fraud is typical with electronic devices. It occurs when a buyer returns an item after dismantling the product and removing its valuable parts. The fraudster will then usually re-sell the parts for a profit, and keep the refund fee issued to them by the merchant.
• Seller sabotage: When sellers buy all the items from a competitor and send them back as late as possible to deplete the competitor’s inventory. Sometimes counterfeit items are returned in the original packaging to damage the competitor’s reputation with legitimate buyers. 
• Stolen merchandise return: This return fraud type occurs when a fraudster uses a stolen credit card to buy an item online before returning the stolen goods in-store for a refund. If the refund is completed on a different card or given in cash, this is an example of placement.

How to detect return fraud?

Since the risk of exposure to fraud grows as companies scale, it is important to implement innovative solutions that can detect fraud in real-time. Measures to proactively detect return fraud include:

• Using machine learning and behavioral analytics to identify anomalistic behavior that indicates various types of fraud.
• Analyzing data from past return fraud cases allows retailers to identify behavioral patterns or red flags specific to their business. This type of information can help sellers spot potential scams and take the appropriate risk-based action to prevent losses.
• Educating and training staff to be able to recognize the red flags surrounding return fraud and explaining what a “normal” vs “abnormal” number of returns looks like.

How to prevent return fraud?

While steps can be taken to prevent return fraud through educating employees, verifying customer identities, and updating policies, companies that take an AI-driven approach are much more likely to stay one step ahead of fraudsters. 

To effectively mitigate the risk of return fraud, firms should:

• Ensure their anti-fraud tools can detect common fraud scenarios and project future risks to help teams anticipate threats. This can be done efficiently and cost-effectively by implementing an AI overlay to existing tools as it does not require a total system overhaul.
• Implement a solution that offers a high level of configurability and provides the ability to build custom rule sets to prevent fraud types that pose a particular threat. 
• Employ a tool that fine-tunes alerts across various payment chains and allows firms to respond to changing fraud risks in near real-time.