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How to stop return fraud

Return fraud is prevalent.

Did you know that 10% of retail returns are fraudulent?

According to the 2022 Consumer Returns in the Retail Industry Report, conducted by NRF and Appriss Retail, fraudulent returns cost the retail industry upwards of $85 billion.

Despite these startling statistics, many retailers are still approaching return fraud and abuse with a one-size-fits-all policy that fails to catch the wide variety of return fraud scenarios and punishes loyal customers. 

To mitigate return fraud and abuse, retailers must develop a stronger understanding of the many ways that bad actors commit fraud at the point-of-return.

Four common types of return fraud

Return fraud can take a variety of forms from shoplifted returns and returns with counterfeit receipts to false order claims, wardrobing and organized retail crime. And return abuse can come in the form of “bracketing,” when someone buys multiple sizes to try the items on at home, or when they return an item that can no longer be resold.

In 2022, the most common form of return fraud reported by retailers was wardrobing. The report from Appriss Retail and NRF found that 50% of retailers had witnessed a customer buying an item with the intent to use it and then return the non-defective item for a full refund. The next most common example, experienced by 41% of retailers, was stolen merchandise being returned for a refund or store credit.

In addition to these common examples, retailers also reported losses from lesser-used strategies like price switching. This occurs when a shopper swaps labels on lower-priced items with higher-priced tags to make a profit on their return refund. 

Similarly, online claims fraud is on the rise because shoppers may illegitimately report that an item did not arrive or was broken upon arrival in the hopes of earning a refund. This type of fraud alone can cost retailers anywhere from $2.1 to $4.2 billion a year.

With so many opportunities to fall victim to return fraud and abuse, it’s important that retailers know how to effectively mitigate even the rarest scenarios. 

The negative impact of restrictive return policies

Today, many retailers combat return fraud and abuse by instituting restrictive policies that aim to catch all fraudulent activities. This can include requiring receipts, shortening return windows, or issuing a maximum number of returns per customer. However, it’s not fair to top customers to receive the same treatment as shoppers who have tried to steal from the retailer. These approaches ostracize loyal customers who do not frequently misuse returns by limiting the retailer’s chance to make amends for a poor shopping experience.

Restrictive policies risk profit losses, negative reputations and a decrease in customer loyalty and satisfaction. Luckily, retailers can rely on artificial intelligence to understand the unique needs of each customer at the point of return and provide dynamic policies and targeted incentives to combat return fraud and abuse effectively while protecting long-term customer loyalty.

The best way to deter return fraud  

While return fraud and abuse may occur in a variety of ways, the instances still typically appear in patterns. One pattern might be from a serial returner who always gives the same email address at the point-of-return. Another pattern could result from an organized retail crime ring that is filing false order claims on items bought by a single credit card across purchasing accounts and delivery addresses.

Regardless of how and where the fraud occurs, retailers can leverage AI and data analytics to detect returns anomalies and suspicious activity that may indicate fraud. Then, once the bad actors have been identified, the AI can recommend a resolution that protects the retailer from unnecessary loss.

This can occur in real-time and at scale, to ensure retailers are always protected. For example, when the serial returner makes their next purchase, they may be issued a warning on their receipt letting them know they will only be able to make one more return within the year.

This individualized approach to return fraud and abuse prevention protects loyal shoppers from restrictive policies. It also provides opportunities to personalize the returns experience in a positive way by automatically granting loyal shoppers longer returns windows or coupons for their next purchase.

With AI, retailers can reduce friction, eliminate return fraud, and enhance the customer experience.

The path forward with AI

Popular return fraud and abuse scenarios may change, but retailers must remain vigilant if they want to protect themselves. The problem will persist until retailers understand and effectively target return fraud in all its forms. With AI and clear customer-centric insights, retailers are better prepared to win the battle against return fraud and abuse, while safeguarding the loyal customer’s shopping experience.

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