What’s the biggest barrier to fighting returns abuse? Is it too-lenient policies? Last-mile delivery issues? Slow claims reviews? In Policy Abuse and Its Impact on Merchants, a new survey of more than 300 global ecommerce merchants by World Business Research (WBR), respondents pointed to a number of challenges: 

  • Most (62%) still rely on manual reviews of returns claims and struggle to identify fraud quickly and efficiently. 
  • Only 19% of respondents say other departments in their organizations understand the challenges of policy abuse very well. 

One of the biggest problems isn’t the policy — it’s old, disconnected ways of data processing that leave the policies open for exploitation.

Disconnected data allows policy abuse to thrive

A customer return engages every single part of your ecommerce flow. When the data sources for each piece of the supply chain are disconnected, your full business operations are vulnerable to policy abuse.

Imagine your ecommerce shop is set up in a large office building with dedicated floors for each department or team. When a customer files a return, that data enters through the front door of the building to the ground floor, where customer service decides how to resolve the customer’s complaint. 

What the customer service data doesn’t show, however, is that this customer actually has five different accounts, which they’ve used to get refunds on five different products that weren’t actually returned. This should raise alarm bells for the investigations team on the top floor, but they only see the cash amount going out the door, not the abusing customer walking out with it.

Customer service still issues the refund, because the entire building has no elevator: each floor is responsible for documenting and maintaining its own data without any visibility into the others. 

What the fraudsters know about your siloed data will hurt you

Serial fraudsters know ecommerce merchants, especially large ones, have siloed data systems for each department. They use this knowledge to exploit return and warranty policies and promotions and loyalty programs,  intended for honest customers. 

The only way ecommerce merchants can combat policy abuse is to see all of the data and find existing patterns. This includes access to data such as: 

  • Comprehensive customer information 
  • Customer purchase histories 
  • Previous returns activity 
  • Specific product details 
  • Reasons for returns 

This aggregated data often reveals subtle fraud signals and suspicious patterns, and empowers merchants to stop abuse in its tracks. “Better data connectivity across channels ensures that we have what it takes to reduce retail fraud and abuse,” said one IT director surveyed.

With readily accessible data across the entire order and return processes, merchants more easily track customer transactions, identify suspicious behavior, and ultimately enforce stronger policy abuse prevention policies.

Data transparency is the solution — so what’s keeping it from happening?

Fraud managers need clear, complete visibility into the entire returns process to identify policy abuse patterns. Uniting company-wide order data is the first, and most critical, step for ecommerce businesses to begin stopping policy abuse. Data transparency also requires a thorough understanding of the timelines, methods, and costs involved at every step of the order,  return, and refund journey.

Data analysis at this scale isn’t new. Marketing and sales teams use interconnected data sources to create customized buyer journeys. So why do ecommerce companies struggle to aggregate data when it comes to policy abuse?

To expose fraud and its indicators, merchants say they need a holistic view of each return, from the moment the customer decides to return a product to the point when it’s received, processed, and finally resolved. 

But simply collecting more data doesn’t completely solve the transparency problem. 

Beyond data collection, automation is key to data transparency

Merchants say they also need to be able to analyze that data quickly to take action.  Most study respondents (94%) who don’t currently have automated systems to address policy abuse are interested in implementing such a system in the next two years. They want efficient data management systems to organize and analyze the information they collect.

According to an IT director from one Mexican company surveyed by WBR, they need “analytical tools that help us expose returns fraud situations” and “automation should be a key feature of the tool.”

Merchants with optimal data visibility can lose cash if they can’t review a holistic view of data fast enough to stop a prolific fraud ring.

Most merchants surveyed (62%) said data analysis in multiple stages of the returns and refund process remained manual, including:

  • Categorizing returns requests
  • Tracking returned items 
  • Flagging potentially fraudulent transactions
  • Documenting the final dispute resolutions 

As long as tasks like these remain manual, fraudsters have more time to exploit return policies.

Overcome the overload: new ways for merchants to use all order data

Many merchants have already taken the important first step of integrating data from their entire returns process. About two-thirds of merchants surveyed (66%) say they are very or somewhat satisfied with their ability to collect data and act on returns abuse. 

Making all order data more visible also means that fraud managers have more data to manage.  But what seems like an overwhelming amount of data is actually an opportunity to make your fraud team even more sophisticated and efficient.  

Once your data is visible and connected, your team can start to:

  • Explore fraud partnerships that offer merchant networks with expanded data to draw upon
  • Uncover who actually makes an order and/or claim, and what  their patterns of behavior are across other sites that signal that they are a risk for abuse
  • Apply and automate data-based decisions
  • Focus on fighting other nuances of policy abuse, such as wardrobing and coupon abuse 

You can prevent policy abuse — once you connect your data

Merchants that optimize data transparency will be able to power more capabilities for combating fraud, including policy abuse, now and in the future. By analyzing historical data and patterns, predictive analytics can enable merchants to forecast possible instances of policy abuse, helping them to be proactive in their approach. 

The research shows that merchants clearly understand that being able to access, understand, and leverage data across an organization’s returns operations is vital to combating policy abuse. It will take automation and AI to realize the full value of data visibility to expose threats and opportunities.

Get a full picture of the state of policy abuse today by reading the full global benchmarks study, Policy Abuse and Its Impact on Merchants. To learn more about how your company can achieve better data transparency in policy abuse, contact us at Riskified.