AI Returns Fraud Detection: Lessons for Payment Security

Returns fraud isn’t a nuisance line item anymore. It’s a $76.5 billion problem for retailers (a figure widely cited in industry reporting around National Retail Federation research), and it spikes right when transaction volume is highest: the holiday season. That’s why UPS-owned reverse logistics company Happy Returns testing an AI fraud detection tool with some clients during the holidays is more than a retail operations story—it’s a signal about where commerce security is headed.

Here’s the stance I’ll take: returns fraud is payment fraud wearing different clothes. The same weaknesses show up—identity gaps, inconsistent data across systems, and “friendly fraud” behavior that hides in normal customer activity. If you work in payments, fintech infrastructure, or risk, you can learn a lot from how reverse logistics teams are starting to treat returns as a high-risk transaction that deserves the same scrutiny as authorization and settlement.

This post is part of our AI in Pharmaceuticals & Drug Discovery series, and yes, that’s intentional. The most useful AI patterns in biotech—traceability, chain-of-custody, anomaly detection, and decision auditing—apply just as cleanly to returns and refunds. In pharma, the cost of an integrity failure can be patient harm. In commerce, it’s chargebacks, margin erosion, and trust decay. Different stakes, same design principles.

Why AI returns fraud detection is suddenly urgent

Answer first: AI is being tested in returns fraud because manual rules and after-the-fact reviews can’t keep up with holiday volume or fraud creativity, and refunds have become a primary loss channel.

Returns are a perfect storm:

• High throughput in Q4 means less time for investigation per return.
• Multiple touchpoints (online order, store drop-off, carrier pickup, refund processing) create data gaps.
• Refund speed expectations have changed—customers expect near-instant decisions.

Happy Returns sits in the reverse logistics flow, which gives it a particularly valuable vantage point: it can see patterns across merchants, products, and return methods (within the boundaries of privacy and client agreements). If you’re designing payment security systems, you’ll recognize the advantage immediately. Fraud detection gets better when you can observe behavior across a network, not just inside one merchant’s silo.

Returns are a “refund transaction,” not a warehouse task

Most companies still treat returns as an operational cost center: scan the item, restock it, issue the refund. That mindset is expensive.

A return is a financial event with all the same risk questions as a payment:

• Is the customer who they claim to be?
• Is the item the same item that was purchased?
• Is the timeline consistent with legitimate behavior?
• Is the refund destination appropriate?

Once you treat the return as a transaction, AI’s role becomes clearer: score the risk, route the workflow, and decide how much friction to apply.

How AI detects returns fraud (and what actually works)

Answer first: The strongest AI approach combines behavioral signals + item-level verification + refund routing controls, rather than relying on a single model score.

A practical returns fraud model rarely looks like a single “fraud/not fraud” classifier. The better pattern is an orchestration layer that uses AI to decide what happens next.

1) Behavior and identity signals (the “who and how”)

Returns fraud frequently shows up as patterns, not one-off red flags:

• High return frequency relative to purchase frequency
• Repeated “item never used” claims for categories that are commonly abused
• Multiple accounts returning to the same address or refund instrument
• Sudden changes in device, location, or contact info before initiating returns

In payments, you’d call this behavioral biometrics and entity resolution. In returns, it’s the same discipline: link identities, devices, addresses, and refund endpoints into a graph, then score the abnormal connections.

2) Item and condition verification (the “what”)

This is where AI can create step-change improvements if it’s implemented thoughtfully.

Common fraud types include:

• Wardrobing: buy, use, return
• Boxing: return a different item (or nothing) in the package
• Counterfeit swaps: keep the genuine item, return a fake

Computer vision can help—but only if you design the capture process. The model needs consistent photos (angles, lighting cues) and a way to compare:

• SKU and serial/lot identifiers
• Packaging characteristics
• Known counterfeit markers

This is also where our pharma series intersects in a very real way. Pharma supply chains obsess over serialization, tamper evidence, and chain-of-custody because verifying “the item is what it claims to be” is non-negotiable. Retail returns are moving in that direction, especially for high-value goods.

3) Refund routing controls (the “where the money goes”)

If you only detect fraud after issuing a refund, you’re already losing.

AI can support a set of routing decisions:

• Instant refund for low-risk returns
• Refund on scan (when a return is physically verified at a drop-off)
• Refund on receipt/inspection for high-risk categories
• Store credit instead of cash refund for elevated risk
• Manual review queue when signals conflict

This is directly analogous to payment routing and step-up authentication. Low risk? Approve quickly. Medium risk? Add friction. High risk? Hold or verify.

A useful one-liner for your risk team: “Fast refunds are a product feature—until they become a fraud feature.”

Reverse logistics is becoming part of the fraud stack

Answer first: Reverse logistics providers are becoming fraud infrastructure because they sit at the intersection of physical verification and financial reimbursement.

Payments teams are used to thinking about risk at authorization time. But returns fraud shows why that’s incomplete. Commerce has multiple “money moments”:

• initial purchase
• refund issuance
• replacement shipment
• store credit issuance
• chargeback outcome

When a company like Happy Returns adds AI fraud detection into returns processing, it’s effectively adding a risk decision point in the refund lifecycle. And that’s where fintech infrastructure comes in.

The bridge to payments and fintech: refunds are a regulated, reputation-sensitive flow

Refunds touch:

• card network rules
• processor policies
• consumer protection expectations
• merchant cash flow

From a fintech infrastructure perspective, returns fraud detection should plug into:

• refund APIs (initiate, void, partial refund)
• case management (evidence, notes, audit logs)
• dispute tooling (chargebacks and representment)
• identity signals (KYC-lite checks for refund endpoints)

If you’re building payment systems, don’t treat refunds as a simple “reverse payment.” Treat them as a separate risk surface with its own abuse patterns.

What pharma AI can teach retail returns (and vice versa)

Answer first: Pharma’s AI discipline around data provenance, auditability, and chain-of-custody is exactly what returns fraud programs need to mature.

This post sits in an AI in drug discovery series, so let’s make the connection explicit and useful.

In pharma and biotech, AI programs live or die on data integrity:

• Where did the data come from?
• Can we reproduce the decision?
• Can we explain why a sample was flagged?

Returns fraud detection has the same requirements, especially when a customer disputes a decision or regulators ask questions.

Practical crossovers that work in the real world

1. Chain-of-custody mindset

   • Pharma tracks samples and batches.
   • Retail should track return custody events: initiated → shipped → scanned → inspected → refunded.

2. Confidence thresholds and human-in-the-loop review

   • In drug discovery, uncertain predictions go to lab validation.
   • In returns, uncertain cases go to inspection or manual review.

3. Model governance and bias controls

   • In healthcare-adjacent AI, governance is strict.
   • In commerce, it’s often loose—and that’s a mistake. A returns model can discriminate unintentionally (location, income proxies, language cues). Governance protects customers and your brand.

4. Explainability for adverse outcomes

   • Pharma needs rationale for trial decisions.
   • Retail needs rationale for refund holds. “Because the model said so” doesn’t survive customer escalation.

Implementation playbook: adding AI without breaking CX

Answer first: The winning approach is to apply AI to routing and evidence collection, not to blanket-deny returns.

If you’re considering AI fraud detection—whether for returns or payment flows—here’s what I’ve found works.

Start with a narrow, high-loss slice

Pick one:

• high-value electronics
• high-abuse apparel categories
• “refund without return” policies
• repeat-return cohorts

Define success with numbers (not vibes):

• reduction in fraudulent refunds (dollars)
• false positive rate (legit customers slowed)
• average time-to-refund
• chargeback rate impact

Build the data spine before tuning the model

AI accuracy is capped by your event data. You need a clean, time-ordered trail:

• order details and payment instrument token (where permitted)
• return method and location
• device and session metadata
• refund destination
• inspection outcomes

The pattern mirrors pharma AI pipelines: data normalization first, model second.

Use “progressive friction,” not blunt friction

A simple policy ladder is often enough:

1. Low risk: instant refund
2. Medium risk: refund on scan
3. High risk: refund after inspection + ID verification for refund endpoint

Customers will tolerate friction when it feels fair and specific. They won’t tolerate random delays.

Instrument for learning, not just enforcement

Every decision should generate a label eventually:

• fraud confirmed
• customer appeal successful
• item mismatch
• counterfeit detected
• policy abuse (wardrobing)

Those labels are your training signal. Without them, the model gets stale fast—especially after the holidays, when behavior shifts.

People also ask: quick answers for teams evaluating AI fraud tools

Is returns fraud detection the same as payment fraud detection?

Not exactly, but the core techniques overlap: anomaly detection, entity resolution, graph analysis, and risk-based routing. The difference is physical verification becomes part of the signal set.

Will AI increase false declines and anger customers?

It will if you use it as an automated “deny machine.” It won’t if you use it to route: instant refund vs refund-on-scan vs inspection. Routing preserves customer experience while still reducing losses.

What’s the fastest path to ROI?

Attach AI decisions directly to refund issuance controls. If the AI output doesn’t change the refund workflow, you’re mostly generating dashboards.

Where this is headed in 2026: refunds as a first-class risk domain

AI testing by companies like Happy Returns is a clue: refund decisions are becoming as engineered as payment approvals. Expect more network-level risk signals, tighter coupling between reverse logistics and payment rails, and more “audit-ready” decisioning.

For teams in fintech infrastructure, the opportunity is clear: build tools that make refunds and returns observable, controllable, and explainable. For readers following our AI in pharmaceuticals & drug discovery series, the parallel is just as useful: the disciplines that keep scientific AI trustworthy—provenance, governance, traceability—are the same disciplines that keep commerce AI from turning into expensive chaos.

If you’re reviewing your 2026 roadmap, here’s the practical next step: map your refund lifecycle end-to-end, identify where fraud losses occur, and decide where AI should route, verify, or delay. The strongest programs don’t try to “catch fraud.” They design systems where fraud has fewer profitable paths.

What would change in your organization if refunds were treated with the same rigor as payments—and every return created an auditable chain-of-custody event?