How to fix a $101 billion problem...
Returns Fraud Part II
In our previous article we discussed how return fraud accounted for a staggering $101 billion in total losses for retailers.
Returns fraud continues to challenge retailers, costing millions in lost revenue and eroding customer trust. We recently explored ways to stop returns fraud and tackle repeat offenders. Building on those insights, this article dives deeper into how technology, integrated data, and a balanced customer-first approach can form a holistic defence against fraud.
Understanding the Multifaceted Nature of Returns Fraud
Returns fraud isn’t a one-size-fits-all problem. While some fraudsters exploit simple loopholes in refund policies, others operate as part of a well-organised network. Consider these scenarios:
Serial Returners: A customer may buy a large volume of items and return most of them. Even if flagged by basic systems, fraudsters can simply create new accounts to bypass detection.
Social Media Stunt Fraud: Some shoppers purchase items solely to post a photo and then return everything—a trend driven by social media aesthetics rather than genuine need.
Digital Exploits: Initial systems that issued refunds based solely on customer declarations without verifying returned items quickly fell prey to abuse. Fraudsters exploited the gap between shipment and warehouse inspection.
Recognising these diverse tactics is the first step. But how do retailers move beyond reactive flagging to proactively safeguard their operations?
Leveraging Technology and Integrated Data
The key to robust fraud prevention lies in connecting the dots—integrating various data sources and using technology to analyse customer behaviour across platforms.
Cross-Channel Data Integration
Retailers often rely on siloed systems where ecommerce platforms, return management systems, and third-party fraud tools operate independently. A more effective approach is to integrate these data streams to build a comprehensive customer profile. For instance:
Account Linkage: By combining email addresses, shipping addresses, and payment methods, retailers can detect when multiple accounts may belong to the same individual. This “spiderweb” of data makes it harder for fraudsters to reset their track record with a new account.
Behavioural Patterns: Machine learning models can track not just isolated incidents but overall patterns—such as unusually high return rates or mismatches between declared and actual returns. This holistic view can identify suspicious behaviour even when individual transactions appear normal.
The Role of Third-Party Fraud Solutions
Some advanced fraud detection platforms extend beyond credit card verification to assess returns behaviour. By feeding in customer data at multiple touchpoints—from checkout to post-delivery—they can offer a risk score that reflects both transactional integrity and returns history. Retailers can then:
Apply Conditional Refunds: For customers with low-risk profiles, refunds can be processed swiftly. For higher-risk profiles, refunds might be held until a warehouse inspection confirms the contents of the return.
Automate Fraud Flags: A system that flags accounts based on integrated data reduces manual review, allowing customer support teams to focus on genuine queries rather than chasing red flags.
Balancing Fraud Prevention with Customer Experience
An overzealous fraud prevention system can inadvertently sour the customer experience. Many retailers face a dilemma: how to protect against fraud without delaying refunds or inconveniencing honest shoppers. Here are some strategies to consider:
A Phased or Tiered Refund Process
Trusted Customers: For shoppers with a clean track record, refunds could be processed immediately upon return dispatch. This not only maintains satisfaction but also reinforces loyalty.
Under Review: Customers flagged for potential fraud can experience a short delay. During this period, an automated inspection process or manual review can verify the return’s legitimacy without lengthy hold times.
BORIS (Buy Online, Return in Store): Allowing customers to return items in-store can be an effective fraud deterrent, as store associates can inspect the return in real-time. This also provides a smoother experience for genuine customers while reducing return-related logistics costs.
Transparent Communication
Informing customers about the reasons behind any delay can help maintain trust. For example, explaining that an extra verification step is in place to protect both the customer and the business ensures that the policy isn’t seen as punitive but as a safeguard.
Looking Ahead: Future Trends in Returns Fraud Prevention
The landscape of returns fraud is evolving alongside technology and consumer behaviour. Here are a few emerging trends that retailers should keep an eye on:
Real-Time Analytics and Machine Learning
Predictive Modelling: By analysing historical returns data, retailers can predict which transactions might be fraudulent even before the order is processed. For instance, if a visitor lands on the site, adds the cheapest available item to their cart, and checks out rapidly, this could indicate bot activity testing stolen credit cards. These behaviours—such as purchasing small, low-risk items to validate payment methods—can be identified through machine learning models that monitor and assess patterns in purchasing velocity, transaction amounts, and checkout consistency. Retailers leveraging predictive analytics can flag and investigate such transactions before fulfilment, reducing the risk of fraudulent purchases.
Continuous Learning: Fraud detection systems that continuously update their models based on new data can adapt to emerging fraud tactics, ensuring that defences remain robust over time.
Closer Integration with Logistics
Instant Inspections: As logistics partners adopt smarter scanning and verification methods at drop-off points, real-time inspection data can trigger immediate refund approvals or alerts.
Data Sharing: Closer collaboration between logistics providers and ecommerce platforms can help bridge the gap between physical returns and digital records, reducing the window for fraud.
Regulatory and Market Shifts
Upcoming regulations—like DPP and those requiring retailers to disclose product return histories—could further transform the returns landscape. In a market where consumers may soon have access to a product’s “return passport,” retailers will be pushed to adopt even more sophisticated fraud prevention measures to maintain both reputation and profitability.
Conclusion
At Commerce Thinking, we've explored practical ways to combat returns fraud and deal with repeat offenders. Building on those insights, a more integrated, technology-driven approach can further strengthen fraud prevention strategies. By leveraging cross-channel data, employing advanced analytics, and maintaining a customer-first philosophy, retailers can protect themselves without alienating legitimate shoppers.
For a deeper dive into combating returns fraud, check out our articles on How to Stop Returns Fraud and Repeat Returns Offenders. Together, these insights and a holistic strategy pave the way for a more secure and customer-friendly ecommerce environment.