How Fashion Brands Lose 2–3% of GMV to Fake Returns on Myntra, Ajio & Flipkart and How to Stop It

Every fashion brand selling on Myntra, Ajio and Flipkart budgets for returns. It is baked into the P&L, factored into pricing, accepted as the cost of doing business in a category where customers routinely order multiple sizes to keep one. What most finance teams do not have a clean number for is the portion of those returns that were never genuinely returned at all: the empty boxes, the swapped garments, the worn-and-sent-back pieces that marketplaces process as legitimate and brands absorb as loss.

Industry estimates put this fake-return leakage at around 2-3% of GMV for apparel sellers, and in some product categories it runs higher. On a brand doing Rs 50 crore a year through marketplaces, that could represent Rs 1-1.5 crore absorbed into returns cost, written off during reconciliation, and rarely interrogated separately from genuine return data.

This piece looks at why fashion brands are particularly exposed to this kind of fraud, why evidence teams already collect often does not hold up in a dispute, and what a claim-ready packing process looks like in practice.

Key Takeaway:

Fake returns cost apparel sellers on Myntra, Ajio and Flipkart an estimated 2-3% of GMV, and most of that loss is recoverable, not because brands lack evidence, but because warehouse CCTV and paper sign-offs are not structured to prove what was packed for a specific order within a marketplace's 24 to 72 hour claim window. Order-linked proof, a short image or video tagged to the Order ID at the point of packing, turns a claim into a lookup instead of an investigation.

See how Ginesys connects order, inventory and fulfilment data to give your team the evidence needed to act on disputed returns.

Understanding Fake Return Fraud in Fashion Retail

Fake return fraud covers any return where a marketplace processes a refund or replacement but the seller does not receive back what was shipped, or receives something unusable, or nothing at all. The most common patterns will be familiar to anyone running marketplace operations: a customer claims the wrong item arrived when the correct one was shipped, and something unrelated comes back instead; a parcel is returned empty despite matching the outbound weight; a garment is worn and returned as "didn't fit"; or a product is reported damaged despite leaving the packing station in resellable condition. Each version follows the same pattern: the claim looks identical to a genuine return, and without proof of what was actually shipped, it can resolve in the buyer's favour by default.

The distinction matters because genuine returns are a cost brands can plan for. Fake returns are a category of loss that, with the right evidence processes, can be more consistently identified and disputed. The gap for most operations teams is that doing so requires order-specific documentation that is not yet standard in most packing workflows.

Why Fashion Brands Are Highly Vulnerable

Fashion sits at an intersection of factors that make return fraud easier to commit and harder to detect than in many other retail categories.

Return rates in apparel are commonly reported in the 20-40% range during normal trading periods and can climb higher during festive sales, which means fraudulent returns can move through at volume without immediately standing out.

Sizing ambiguity gives any fraudulent claim a natural cover story: "didn't fit" is difficult to dispute without packing-level proof of what was shipped.

High SKU counts and frequent style refreshes mean warehouse teams are often processing large volumes of near-identical items, making item-by-item manual verification difficult to maintain consistently at scale.

Marketplace return windows are also relatively tight, which limits the time available to contest a claim even when irregularities are suspected.

The Hidden Impact on GMV and Profitability

The 2-3% figure may understate the full financial impact, because fake returns tend to create a compound loss. The unrecovered product is one part; the acquisition cost, platform commission, forward logistics and packaging already spent on an order that returns nothing usable is the other. There is also a less visible consequence: brands whose return data does not separate fraud from genuine dissatisfaction can draw the wrong conclusions, reviewing sizing or quality control when the actual problem sits in evidence and claims infrastructure.

Before you can prove what shipped, you need a retail ERP that captures order, inventory and dispatch data with enough granularity to defend a claim.

Why Manual Evidence Fails Against Marketplace Claim Rules

Myntra, Ajio and Flipkart each offer sellers a way to dispute fraudulent returns, but the process is stacked against anyone who can't produce order-specific proof fast. Claim windows are tight.

Ajio often requires evidence within 24 to 48 hours, Myntra close to 48, Flipkart 48 to 72, and the bar is specific: proof has to show the exact item packed for that exact order, not a general sense that the warehouse "usually gets it right." Claims get rejected almost entirely for evidentiary reasons rather than factual ones: footage that isn't timestamped or order-linked gets waived off as unverifiable, and claims filed with no packing-stage documentation at all are essentially undefendable.

This is where many brands' existing evidence setups run into difficulty. Warehouse CCTV records a location continuously rather than a specific transaction, which makes isolating footage for one disputed order, within a short claim window, time-consuming and often impractical. Packing staff sign-off works as a control, but paper-based records can be hard to retrieve accurately during high-volume trading periods. By the time a flagged return reaches someone who can locate and submit the evidence, the window may already have closed.

The result is that evidence often exists somewhere in the operation, but not in a form that can be located, matched to a specific order, and submitted within the time available.

Disclaimer: Figures mentioned above reflect general marketplace practice at the time, and are indicative only. Claim windows can change, may vary by product category, dispute type and seller account status, and should be verified directly with each platform before a claim is filed.

Order-Level Proof: A More Effective Approach

Rather than additional surveillance coverage, what tends to hold up in disputes is evidence structured around the specific order rather than the broader location. That means every SKU packed generates a short image or video, captured before the parcel is sealed and tagged with the Order ID, SKU and timestamp at the point of capture. When a return is disputed, that proof functions as a lookup rather than an investigation: the relevant clip or image for that order can be retrieved without manually searching through continuous footage.

Order-linked evidence is considerably harder for a marketplace to reject on evidentiary grounds than general CCTV footage, particularly when it shows the specific product packed for that specific order at a recorded time. For teams processing large volumes of near-identical SKUs, having that proof accessible at claim speed is one of the more practical measures available for reducing the proportion of disputes that go uncontested.

See how Ginesys OMS links packing evidence to individual orders, so your team can retrieve proof without searching through hours of CCTV footage.

Packing Station Best Practices to Follow

A few habits help teams build a more defensible evidence trail.

• Capture image or video proof before the parcel is sealed, so it shows the product in its final verifiable state.
• Tag evidence to the Order ID automatically at the point of capture rather than matching it manually afterward, since manual matching is where delays tend to accumulate.
• Treat evidence capture as a required step in the packing sequence rather than an optional one, particularly during peak trading periods when dispute volumes are also typically elevated.
• Set evidence retention to cover the longest active claim window across all marketplaces, so records are not purged before they are needed.

None of this requires rebuilding the packing process. Evidence capture works most effectively when it is embedded into the sequence that already exists, making proof a byproduct of standard fulfilment operations rather than a parallel initiative.

Behind every disputed claim is a packing-floor decision made weeks earlier. Whether a brand wins or loses a fake-return dispute is usually decided at the packing station, long before the claim is ever filed, by whether the evidence existed and was retrievable in time.

Want a second opinion on your evidence process?

Talk to a Ginesys expert about your packing, claims, and marketplace return workflow.

The Business Case: Evidence Processes, Recovery Rates and ROI

Addressing this at scale involves connecting the order management layer, the packing process and the marketplace claims workflow, rather than treating evidence capture as a standalone warehouse activity. Automation is relevant here because speed is the core constraint. A claims process that requires someone to manually locate and upload footage within a 24-hour window is difficult to run consistently at high dispute volumes, and brands selling across multiple marketplaces at scale typically encounter more disputes than manual processes can reliably handle.

Most brands can report their overall return rate. Fewer track claim recovery rate as a separate metric: the proportion of disputed returns where a claim was filed and resolved in the seller's favour. That figure is a more direct reflection of whether fake-return losses are being actively managed, and brands that begin measuring it separately often find the recovery rate is lower than initially assumed, primarily because claims were not filed within the available window.

The ROI case follows directly: if fake returns account for 2-3% of GMV and an order-linked evidence process recovers even half of that through successfully contested claims, the exercise pays for itself well before counting the secondary benefits of fewer write-offs and cleaner reconciliation, because it largely layers onto packing operations that already exist. The potential ROI depends on order volume, return rates, dispute frequency, recovery rates, and implementation costs.

Checklist to Reduce Fake Return Losses

• Capture image or video evidence at every packing station before parcels are sealed.
• Tag all evidence automatically with Order ID, SKU and timestamp at the point of capture.
• Store evidence in a system searchable by Order ID, not by camera or shift.
• Align evidence retention to the longest claim window across all marketplaces sold on.
• Classify every disputed return as suspected fraud or genuine at the point of flagging.
• Track claim filing rates and outcomes separately from overall return rates.
• Train packing staff on evidence capture as a mandatory, non-negotiable SOP step.

As marketplace platforms continue to develop their own fraud-detection processes, the evidence bar for sellers is likely to rise over time. Brands that build order-linked proof into standard packing workflows now are likely to be better positioned for that shift, with less evidence reconstruction required when disputes arise.

How Ginesys Helps Fashion Brands Close the Fake-Returns Gap

Most fake-return losses don't happen because a brand lacks evidence; they happen because the evidence exists somewhere disconnected from the order it needs to defend. Fixing that is a systems problem before it's a warehouse-discipline problem.

Ginesys OMS embeds this directly into the return processing workflow through its Video Evidence module. Each return order is scanned, and quantities are captured on video in multiple views: label, package, front view, and others, with every recording linked to the specific Order ID at the moment of capture rather than matched manually later. The module runs on phone, desktop, and local warehouse CCTV, which means evidence capture scales across different fulfilment setups without requiring dedicated hardware. When a fraudulent return claim comes in, the team isn't searching for what was dispatched. The footage is already there, already tagged, already tied to the order in question.

Ginesys OMS consolidates order, return and dispatch data across marketplaces including Myntra, Ajio and Flipkart, so claims teams working within tight windows have the relevant order history and supporting documentation accessible from one place.

For brands selling across multiple platforms at meaningful volume, having proof that is structured and retrievable from the point of dispatch, rather than assembled after a dispute is raised, can make a material difference to claim outcomes.

For fashion brands running marketplace volumes, fake-return losses are an area where better evidence processes have the potential to translate into measurable claim recovery. The clearest starting point is tracking claim filing rates and outcomes as a separate metric from overall return data. That is where the gap between what was lost and what was recovered becomes visible, and where the business case for investment becomes concrete.

Ready to make every packed order claim-ready? See how Ginesys Warehouse Management keeps packing, evidence, and dispatch data connected.

Frequently Asked Questions (FAQs)

How much GMV do fashion brands typically lose to fake returns on Myntra, Ajio and Flipkart?

Industry estimates put the figure at around 2-3% of GMV for apparel sellers, with some product categories running higher, particularly where sizing ambiguity is more common. On meaningful marketplace volumes, this represents a significant and often unmeasured annual cost.

What's the difference between a genuine return and a fake return claim?

A genuine return reflects a real sizing, quality or preference issue where the seller gets back what was shipped. A fake return is processed identically by the marketplace but involves a wrong, used, damaged, or missing item the seller never actually recovers.

Why do marketplace claim windows make it difficult to dispute fraudulent returns?

Ajio, Myntra and Flipkart typically require evidence within 24 to 72 hours of a flagged return, and that evidence must be specific to the individual order. Sellers should verify current requirements directly with each platform, as these can change. Without pre-captured, order-linked proof, it can be difficult to retrieve and submit valid evidence before the window closes.

Why is warehouse CCTV often insufficient to win fake return disputes?

General CCTV records a location continuously rather than capturing individual transactions, which makes locating footage for a specific order within a short claim window time-consuming. Because it is typically not searchable by Order ID, it can be difficult to use as valid evidence even when the footage technically exists.

What is order-level proof and why does it matter?

Order-level proof is packing evidence, typically a short image or video, captured and tagged against a specific Order ID at the point of dispatch. It is the format marketplaces can act on in a dispute, and because it is linked to the order rather than a location, it can generally be retrieved far more quickly than footage from a continuous recording.

How quickly can a fashion brand see ROI from dispute-proof packing operations?

Because the process largely layers onto existing packing operations, brands running meaningful marketplace volume typically see payback within months, recovering a share of the 2-3% GMV leakage through successfully contested claims.