Online shopping demands a return experience that mirrors the purchase process: simple, fast, and friction-free. But that same expectation comes at a high cost. In 2024, returns drained more than $100 billion from global retailers, a figure that continues to climb as shopping habits evolve.
Retailers now face a difficult balancing act: maintaining positive customer experiences while cracking down on increasingly sophisticated forms of fraud. Wardrobing, where items are worn once and sent back, and bracketing, where multiple versions are purchased with the intent to return most, are just two examples of behaviours that look legitimate at first glance but cost retailers dearly.
Returns used to be treated as a customer service issue. Now, they’re a business-critical challenge.
AI in Return fraud detection: smarter, not stricter
Advanced analytics and machine learning are giving retailers a better way forward. Rather than relying on rigid rules, intelligent systems can spot subtle inconsistencies and patterns in customer behaviour, purchase history, and product data. This approach doesn’t just identify fraud after it happens, it helps prevent it in the first place.
One of the most promising developments is the use of intelligent tools at the pre-purchase stage. These systems can help guide customers toward the right product before they even click “buy.” Based on previous return behaviour, sizing patterns, or even preferred brands, shoppers can be shown alternatives that better match their needs; reducing the likelihood of a return altogether.
Post-purchase, AI steps in again. Sophisticated models can identify when an item is likely to be returned, or when a shopper’s return habits start to diverge from the norm. A customer who returns every third order with the same justification might raise a different flag than someone who’s returning for the first time.
Crucially, these systems can tailor their response. Rather than blanket rules that risk alienating loyal shoppers, retailers can take proportionate actions, such as delaying instant refunds, introducing tracked returns, or adjusting recommendations.
It’s not about being punitive. It’s about building smarter systems that treat each transaction in context.
From return centre to sorting line: automating the back end
While fraud detection gets most of the attention, there’s also huge potential to streamline the physical processes that come after an item is sent back. Return centres are often resource-heavy, relying on staff to inspect, grade and decide what happens to each product. This is time-consuming and difficult to scale.
Automated systems can change that. By recognising product types and conditions, AI can support faster, more consistent grading. Items can be flagged for restocking, resale, repair, or recycling with minimal human involvement. Some tools even integrate image recognition to identify wear, damage or authenticity, though these are still expensive to roll out widely.
Even so, the direction is clear. Reducing reliance on manual SOPs helps speed up processing and ensures returned items are dealt with more efficiently. That can have a major impact on profitability, especially in categories like apparel, where margins are tight and return rates are high.
As labour costs rise and staff shortages persist across supply chains, investing in smarter automation is no longer optional, it’s necessary.
AI needs data to be effective
None of this works without strong data. To make intelligent decisions, retailers need full visibility into returns. That means understanding what’s being sent back, when, by whom, and why.
Unfortunately, this is still a blind spot for many businesses. Return data often sits in separate systems or is tracked inconsistently. But when it’s pulled together and analysed properly, it can reveal a lot. A spike in returns on a certain product line might signal more than just fraud, it could point to sizing issues, misleading photography on the website, or even damage in transit.
By treating returns as a feedback mechanism, brands can uncover valuable insights that inform everything from marketing to product development. That’s where AI really earns its place; using patterns, not just policies, to make better decisions.
In my work with retailers around the world, I’ve seen how small adjustments informed by return data can dramatically reduce return rates. For instance, flagging high-return regions or tweaking product descriptions based on sizing complaints can have a measurable effect on profitability.
Fraud might be the headline problem, but operational improvement is often the hidden benefit.
Returns as a sustainability lever
Returns also have a significant environmental footprint. Extra shipping, repackaging and restocking all come at a cost; not just financially, but ecologically. Smarter systems can reduce waste by ensuring that returned items are handled efficiently and resold where possible, instead of ending up in landfill or storage limbo.
Transport optimisation is another area with untapped potential. When return routes are guided by real-time data, businesses can consolidate shipments, reduce vehicle mileage, and cut emissions. That aligns well with sustainability goals and investor expectations, especially as environmental reporting becomes more standardised.
There’s also room to improve customer-facing transparency. Helping shoppers understand the impact of their return behaviour—through gentle nudges, return impact scores, or delivery estimates based on restocking timelines—can help shape better habits over time.
In this way, returns technology isn’t just about loss prevention. It’s about building a healthier retail ecosystem.
What’s next?
The next few years will bring even more integration of intelligent tools across the e-commerce journey. We’ll see greater use of real-time fraud scoring, tailored policies based on return risk, and deeper links between returns and inventory planning.
Personalisation will become more predictive. If someone frequently returns certain colours or styles, those preferences can be filtered out before they even browse. Likewise, retailers will use past return data to recommend the best size or variant for new customers with similar profiles.
The technology that handles post-return processing will also improve. Expect more automation at grading stations, particularly for higher-value items where resale is a key part of the recovery strategy. These systems will continue to mature, making it easier to assess items quickly, assign a resale channel, and recover value.
Meanwhile, the distinction between fraud prevention and customer experience will continue to blur. The most successful retailers will be those who use technology not to say “no,” but to guide customers toward better choices; before, during, and after purchase.
This is about trust, not just tech
AI is an enabler, not a silver bullet. Fighting return fraud isn’t just about algorithms. It’s about trust; knowing which behaviours to accommodate, and which to challenge.
If we get it right, we don’t just save money. We preserve the customer relationships that good businesses are built on.
Fraud is evolving. But so are the tools we have to fight it. The future of returns is not simply about tighter policies or faster logistics. It’s about building systems that are intelligent, adaptive, and fair for everyone involved.
