Return fraud has become one of the fastest-growing risks in retail. With nearly one in six returns classified as fraudulent, what was once a marginal cost of doing business now drains over $100 billion from merchants each year.
Part of the headache is the increase in volume; the bigger threat is how quickly fraudster playbooks are evolving. Behaviors like wardrobing, empty-box claims, and serial return abuse now sit alongside more sophisticated tactics powered by synthetic identities and AI-driven automation.
Unsurprisingly, traditional fraud models that excel at spotting anomalies at checkout struggle to interpret today’s much messier world of post-purchase behavior. Solving this widening gap will require AI that can understand identity, intent, and behavior, not just at the moment an order is placed, but across every step that follows.
Why return fraud is accelerating
To understand why return fraud has spiked so quickly, it helps to look at the pressures building beneath the surface:
- Shoppers are behaving differently, and policies haven’t kept up: With buy now pay later (BNPL) reinforcing a “try before you commit” mindset, bracketing, wardrobing, and high-frequency returns have become normalized, especially in apparel. According to recent research, 69% of shoppers admit to wardrobing (up 40% from the prior year), while more than half of Gen Z buyers routinely order multiple sizes with the intent to return.
- Self-service infrastructure widened the attack surface: Automated portals, QR drop-offs, “no box/no label” returns, and instant refunds may solve real CX problems, but they also eliminate natural verification points. Many refunds now hit customer accounts before an item is scanned, let alone inspected. Online return rates have ballooned to 24.5%, nearly three times the 8.7% in-store rate, and the infrastructure isn’t built to tell the difference between a legitimate return and a calculated one.
- Fraudsters are adopting automation, too: The other shift is happening on the fraudster side. Tactics that once required time and manual effort can now be multiplied with basic automation, like templated refund messages, lightly modified identities, and repetitive claim sequences that look human at a glance.
Why legacy fraud systems aren’t built for this moment
Return fraud exposes a gap that’s been hiding in plain sight: retail systems were never designed to interpret what happens after a purchase.
Checkout fraud produces clean, immediate signals like mismatched addresses, odd velocity, and suspicious devices. Post-purchase abuse, on the other hand, unfolds over days or weeks and is scattered across carrier scans, warehouse inspections, CX conversations, and policy exceptions.
Identity makes the problem even harder. Retail still depends on signals such as emails, devices, and order numbers, but today’s shoppers and fraudsters move fluidly across multiple accounts, addresses, and payment methods. What looks like five unrelated customers can easily be one actor exploiting those gaps.
Rules engines haven’t kept up either. Static policies like “deny after X returns” or “flag without tracking” break down in an environment where bad actors routinely probe for thresholds. Over 80% of retailers tightened return policies in the past year to combat fraud, yet industry data shows these changes have had little effect on deterring abuse.
And when systems fail, the burden shifts to people. CX teams, warehouse staff, logistics, and fraud analysts are left reconstructing claims long after the fact, which is an inconsistent, labor-heavy process that often costs more than the item in question. During busy seasons, those pressures only intensify, and fraudsters know exactly when to take advantage.
What the next generation of return fraud AI must solve for
Solving return fraud at scale requires new models that go beyond isolated events and capture how customers, carriers, and systems behave over time.
Behavioral patterns that unfold over weeks
Return abuse is rarely a single bad moment; instead, it builds slowly, think: a shopper who “borrows” outfits a few times a year, a customer who develops a pattern of item-not-received claims, or a synthetic identity that blends legitimate purchases with carefully spaced disputes.
This is why sequence-based AI modeling is essential. Instead of treating each return request as an isolated data point, tools look at velocity, order diversity, timing trends, and historical dispute patterns to pinpoint the difference between high-volume but honest customers and low-volume accounts that quietly cause outsized risk.
Siloed systems that can’t see the full picture
The signals needed to evaluate a suspicious claim often exist; unfortunately, they’re just scattered across systems that don’t talk to each other. For example, the carrier might log an unusual weight, or perhaps the warehouse flags an empty box. Or maybe CX notes a complaint filed before the item even shipped. Individually, each signal appears benign, but together, they indicate major red flags.
Modern AI needs to act as a real-time orchestration layer capable of integrating carrier APIs, warehouse scans, CX transcripts, and payment data to reconstruct what actually happened in the moment a refund decision is made, rather than hours or days later.
Identity fragmented across channels
For years, retail identity has been little more than an email, a device ID, or a credit card token. That fragility wasn’t a problem when fraudsters worked one account at a time. Nowadays, a handful of synthetic profiles or recycled addresses can mimic normal customer behavior, and without durable identity resolution, the system sees a cluster of unrelated individuals instead of a coordinated pattern.
Retailers heading into 2026 need AI capable of piecing together the threads across shipping patterns, behavioral signatures, timing correlations, and product affinities to reveal when five “customers” are actually one actor. When five accounts share the same behavioral fingerprint, AI should be able to catch it long before a human ever could.
Ambiguity that defies yes/no logic
One of the reasons return fraud is so expensive? Teams are stuck making yes/no choices on problems that don’t cleanly fit into yes/no categories. Most return requests are ambiguous: a refund that looks risky may be legitimate; a refund that looks normal may be deeply coordinated abuse.
Next-generation AI must move beyond rigid rules to evaluate risk as a spectrum. This allows refund timing, documentation requirements, and routing to adapt dynamically based on confidence levels.
The future of returns must satisfy both customers and sellers
Return fraud doesn’t happen in isolation, and neither should the tools meant to stop it. Rather than implementing tougher policies, retailers built for this moment are leveraging unified risk engines powered by AI that can tie together order histories, carrier events, CX transcripts, warehouse scans, product metadata, and past outcomes into a single decision layer.
The payoff is fewer losses, but more importantly, it’s the ability to stop penalizing honest customers for system blind spots — and to finally distinguish genuine friction from deliberate abuse.
