How to Identify Return Fraud: Tips & Best Practices to Protect Your Business
Most return requests you receive will be legitimate. So, it can be easy to simply miss the fraudulent ones, let alone distinguish them from the real deal.
Take a closer look, though, and you’ll find that scammers often leave behind a trail of digital and behavioral clues. They might say they’re in one location but ship an item to another location instead. Or, they may show an unusual pattern of frequent returns, or tell stories that just don’t add up.
In this chapter, we’ll point out a few red flags and data points to look for so you can spot return fraud before it’s too late.
Combating return fraud requires merchants to employ some specialized detection tools, as well as tactics specific to pick out return fraud. Examples of these tactics include:
Analyzing Digital Footprints
Merchants can analyze customers’ social media presence, device information, IP addresses, location, and browsing history for signs of suspicious activity. Disparities between the information a customer provides to the merchant and information about the customer elsewhere on the web may signal that the buyer is a high-risk candidate.
Analyzing active and passive digital footprints can also help merchants flag buyers who make purchases using temporary email addresses, or who sign in with virtual private networks (VPNs). While not automatically fraudulent, these tactics are commonly used by bad actors interested in carrying out return scams, and should be marked as red flags.
Merchants can use behavioral analytics tools to monitor customers’ devices and IP addresses for signs of sudden changes in spending habits or product preferences. These methods can be coupled with machine learning techniques to parse huge volumes of transaction data in real time for signs of outlier activity or unusual trends.
When deployed correctly, these models can detect both pre-transaction (e.g. payment card fraud, identity theft, etc.) and post-transaction (e.g. refund fraud, chargeback fraud, etc.) risks.
Examining Return Activity & Inventory Data
Merchants who analyze previous return transactions are likely to notice that some items are more frequently returned than others. A subset of these items may be of interest to scammers who want to carry out return fraud.
In a similar vein, merchants who maintain an updated inventory can counter-check sold and returned items for signs of suspicious post-transaction activity. Thoroughly inspecting returned products for signs of damage can also help sellers identify fraudulent returns.
Monitoring Customer Communication Patterns
Fraudsters often avoid direct phone contact, preferring chat or email where they can craft responses carefully and avoid real-time questioning. They typically provide vague problem descriptions like "item didn't work" without giving any specific details, rush the return process by expressing unusual urgency, or get evasive when asked for photos, serial numbers, or detailed explanations of product issues.
Pay attention to customers who use different communication channels for purchases versus returns, too.
Implementing Return Velocity Monitoring
Set automated alerts for customers who return items more often than normal. For example, more than three returns within 30 days, returns exceeding 50% of total purchases, or unusually high return values compared to purchase history. These thresholds should trigger enhanced verification procedures before processing additional returns.
Consider tiered monitoring that accounts for customer lifetime value and purchase patterns, as legitimate high-volume customers may naturally have higher return rates. The system should also flag suspicious timing patterns, like returns clustered around promo periods or just before return deadlines.
