Retailers have always had a hard time with returns, especially after the holidays. Abuse of refunds is becoming an even bigger problem as a result of the pandemic’s increase in online shopping, and experts predict that this problem will only get worse.
In 2020, cybercrime cost more than $1 trillion worldwide, according to Atlas VPN, and Sift Trust and Safety Architect Jane Lee anticipates that figure will rise by the end of the year. Accertify’s VP of Marketing & Client Solutions, Jeff Wixted, provides some important reasons why the problem of refund abuse won’t go away anytime soon:
- Expansion of e-commerce. An increasing number of products are being purchased online and delivered to individual households. Retailers may have a harder time catching fraudsters as the volume grows, making it easier for them to hide.
- First-Party Betrayal. When the “true” customer is making the purchase, it is extremely difficult to identify 1st Party Fraud. In the end, this kind of attack happens after the goods have been received, not when they are purchased.
- Retailing in a competitive environment The return and refund policies of a retailer influence many customers’ shopping decisions. Consequently, balancing a customer-friendly policy with protection against abuse and fraud is a delicate balance.
What to Look Out For During the Holidays
According to Lee, bad actors are known to rig the refunding process by trying to return a stolen item, “returning” an empty package, or falsely claiming that they never received a shipped item. Buy online, pick up in-store fraud should also be prepared for by retailers. Since the beginning of the pandemic, BOPIS shopping has increased dramatically, making it an appealing method for fraudsters to take advantage of. It is easy for BOPIS fraudsters to go unnoticed because merchants cannot use the buffer time between an order being placed and shipped to validate a transaction or rely on traditional indicators like a mismatch between a billing and shipping address, as they would with a typical online order. Lee stated, “Retailers need to look to other forms of data to understand the usual behaviors of their customers and spot telltale deviations to mitigate this challenge.”
According to Lee, apparel and electronics are among the most common targets for fraudsters, and the increased volume of transactions occurring during the holiday season only provides additional cover for dishonest individuals to slip under the radar. Additionally, fraudsters are pursuing higher-value purchases. Sift recently discovered a 69 percent increase in the average value of attempted fraudulent purchases, with designer brands and new electronics among the most popular targets.
How Can Retailers Combat Fraud?
According to Wixted, “it is important to start monitoring and measuring” because “one of the main issues with refund abuse is that so many merchants do not know the true size of refund fraud within their organization.” The fraud prevention teams ought to collaborate with the customer service and support teams to keep track of how many requests for refunds are coming in, particularly for large-ticket items. Make sure that these requests are recorded each time they are made so that they can begin determining which customers have filed multiple claims. Merchants can determine the true impact and the best way to mitigate the problem by measuring the size of the problem, which is always the first step.
Lee recommends starting with a Digital Trust & Safety strategy, which dynamically addresses fraud while creating a more seamless experience for legitimate customers, if your company does not already have a dedicated fraud prevention team in place. During times of high traffic, traditional manual review procedures, such as caps on order values and volumes, are unable to detect fraud. Instead, they are either completely halting legitimate transactions or causing customer journey friction. This holiday season, retailers can better defend against fraud at scale by implementing a Digital Trust & Safety approach and technologies like machine learning. Machine learning systems can quickly adapt to detect suspicious activity in real time without human intervention by ingesting thousands of distinct signals beyond purchase data.
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