When the pandemic forced British high-street stores to shutter globally, it led to an unprecedented surge in online retail. In the UK, for instance online retail penetration surged from 20% to over 35% during 2020–2021 alone, according to the ONS. This boom brought with it a new set of customer expectations, specifically; fast, easy, and no-questions-asked returns.
But what began as a mutually-beneficial goodwill gesture that kept things running smoothly, quickly morphed into a costly vulnerability. “Wardrobing”—buying with the intent to use and return—became common. So too, did false claims, refund scams, and policy exploitation, which cost UK retailers more than £1bn last year alone. Naturally, many consumers blame the retailers!
Just as retailers adapted to a new reality, so too did bad faith actors and fraudsters.
When Convenience Meets Consequence
According to the National Retail Federation, return fraud accounted for an estimated 10.4% of total returns in 2022, at the end of the pandemic, while the ONS revealed a 19 per cent increase in retail fraud in 2024, reaching nearly a million incidents.
From “rocks in a box” scams—where customers return packages filled with junk instead of merchandise—to false “item not received” claims, return-related fraud evolved in both scale and sophistication. In response, retailers like Zara, H&M, and Boohoo introduced return fees, tightened return windows, added processing delays, or flagged customers for excessive returns.
However, this new reality also posed a difficult balance for retailers to strike. They risked punishing valued customers and undermining their loyalty. And, if they didn’t have an effective returns policy, it would not resolve the problem either.
Why Blanket Policy Changes Often Backfire
In the fight to protect profit and cut the volume of potentially fraudulent or abusive claims, big retailers have opted for rigid return policies. With over half (52%) of UK shoppers less likely to return to a retailer if a refund takes a long time to receive, more stringent policies also cut down on process and issuance time.
Yet in the new retail reality, it’s not only trust and satisfaction which will be impacted by changes in policies. Operational structures of most retailers leave return processes siloed within customer service teams, which are disconnected from fraud teams.
This means an open vulnerability to fraud, with some retailers lacking the data and tools to differentiate between loyal customers’ using legitimate returns, and abuse.
Toward A Balanced Approach: Intelligence Over Instinct
So, how do retailers break free from the reactive pendulum swing between total leniency and blunt enforcement? The answer lies in leveraging Data, AI and behavioural insights.
Or, put more plainly, machine learning models that can evaluate customer intent and risk at both checkout and claim stages. This allows retailers to block known abusers or approve trusted customers, avoiding unnecessary delays or denials, which harm customer experience. It also joins up the returns with fraud processes, which in the longer term can save retailers money.
Critically, retailers can impose dynamic return policies, so enforcement is based on purchase history and return behaviour. Recent data shows, that repurchase rates rose by 132% for customers who had a seamless return experience.
Returns Built on Trust
No retailer’s goal should be to make returns harder for consumers; it should be to make them smarter. Equipping customer service teams enables them to respond faster and more accurately, reducing the need to escalate every single case to dedicated fraud teams or experts. While mistakes will still occasionally be made, this method gives instant refunds to those that are valued over a long period, and addresses risks with a longer term view.
Retailers don’t have to use returns policies as a blunt tool either. There are alternative methods for resolving return issues. These include offering store credit for specific cases, or
initiating a longer-term review for suspicious patterns. This helps to preserve good customer relationships while limiting exposure to repeat offenders.
Protecting the bottom line without breaking the bond Retailers don’t have to choose between protecting profits and keeping customers happy. By combining machine learning with smart segmentation and operational alignment, returns policy actually becomes a lever for growth—not just loss prevention.
Intelligent return policies empower retailers to significantly cut fraud losses while simultaneously building trust and satisfaction with genuine customers, ultimately driving
long-term customer value and loyalty. In today’s competitive retail environment, trust isn’t just built at checkout—it’s cemented at the return.
Protecting the Bottom Line Without Breaking the Bond
Retailers don’t have to choose between protecting profits and keeping customers happy. By combining machine learning with smart segmentation and operational alignment, the returns policy actually becomes a lever for growth. Not just loss prevention.
Intelligent return policies empower retailers to significantly cut fraud losses while simultaneously building trust and satisfaction with genuine customers. Ultimately driving long-term customer value and loyalty.
In today’s competitive retail environment, trust isn’t just built at checkout—it’s cemented at the return.