Customers have been trained to expect an easy and lenient return and refund experience — and merchants consider their generous policies an essential part of doing business. Unfortunately, those policies also make merchants vulnerable to policy abuse that can be extremely costly. 

To identify policy abuse, merchants have to put in significant time and effort to review returns claims. However, new research says most are doing it the wrong way, with manual workflows that slow the process, consume resources, and let fraud slip through the cracks. 

World Business Research (WBR) took a closer look at this situation in Policy Abuse and Its Impact on Merchants, an in-depth study of the current state of fraud among ecommerce merchants. The survey of more than 300 global merchants revealed the stark reality: many retailers simply don’t have the right automation in place to protect themselves from costly fraud. And the automation problem starts with a data problem.

Manual Reviews Are Still the Norm — and That’s a Problem

The research reveals a clear operational bottleneck hindering merchants’ abilities to stop returns fraud

Most merchants in the study claimed they still rely on manual reviews to process a majority of their refund and return claims, a time-consuming and error-prone process. Some said they rely entirely on manual reviews. As a result, most say it takes three days or more to process claims, with some taking longer than a week. This shows how slow policy abuse fraud can be, as well as how long customers may wait to receive a refund. The result? Retailers with automated fraud management solutions will have a competitive advantage.

Bringing automation, including machine learning, into the returns process enables merchants to identify fraudulent activity faster, improve the customer experience, and redirect staff to more strategic activities like data analysis and generating new business insights.

The Automation Gap: Merchants Without Automation Can’t Collect the Returns Abuse Data They Need

According to the study, most merchants aren’t satisfied with their ability to collect data and act on returns abuse.  Data is the most important tool at merchants’ disposal to identify return fraud. Without automation, merchants miss out on the insights they need to take quick action against policy abusers.  

Data can reveal patterns, trends, and the overall scope of the problem to guide anti-policy abuse strategies. Data also fuels the machine learning tools required to fully and efficiently enforce returns policies. Data points such as the customer’s purchase history, the items in question, and payment details can help merchants identify abnormal patterns and problematic customers. 

For example, if a customer returns an item with signs of wear and tear that don’t match the claim they are making about the return, that could be a red flag for “wardrobing” or another type of policy abuse. If merchants have the right data tools in place, they can collect and analyze information quickly and make informed decisions about the return before the refund is out the door. 

Most Merchants Lack Automated Fraud Prevention — They Want AI

Merchants need automation tools to identify fraud quickly and process returns faster. Yet the research shows that most merchants don’t have automated systems to identify and address policy fraud and abuse, which is remarkable considering the widespread use of automation in other areas of ecommerce.

Automated returns management solutions use machine learning to quickly analyze customer and transaction data to assess the likelihood that a return claim is fraudulent or noncompliant with merchant policy. Automation can also identify fraudulent chargebacks, automatically resolve customer disputes based on available data, and reveal the identities of fraudsters based on previous behaviors — all with more speed and accuracy than manual processes allow.

Not surprising, survey respondents indicated that AI and better data intelligence will be key to reducing abuse. Many said they need to free up resources to analyze their policies and identify the loopholes customers are using to perpetrate it. Most merchants without an automated system for fraud detection said they were interested in implementing one in the next two years. 

With the costs of ecommerce fraud rising and fraudsters becoming more sophisticated, there’s really no time to lose.

Close the Door on Returns Fraud

By leveraging automated tools, better data intelligence, and AI, retailers can save time and money on returns processing, free up workers for tasks that drive growth, and preserve generous policies that loyal customers expect. 

Sign up to receive the full study: Policy Abuse and Its Impact on Merchants. To learn more about how Riskified can help you leverage machine learning, automation, and our deep expertise to fight fraud, speak with the Riskified team today.