Visual Returns Fraud Prevention
For retailers processing 10K+ returns daily. Detect fraudulent returns with visual verification. 85% fraud detection, 60% reduction in return abuse.
E-commerce retailers and omnichannel brands experiencing high return rates and fraud who need to verify return authenticity at scale
Return fraud (wrong items, damaged goods, counterfeit swaps) costs retailers $25B annually. Manual inspection cannot scale and misses sophisticated fraud
Ready to implement?
Why Mixpeek
85% fraud detection accuracy, 60% reduction in return abuse losses, and automated verification at warehouse speed
Overview
Return fraud is a growing problem for e-commerce. This use case shows how Mixpeek verifies return authenticity through visual comparison.
Challenges This Solves
Swap Fraud
Customers return different or counterfeit items
Impact: $25B+ annual industry losses
Volume Pressure
Thousands of returns daily need processing
Impact: Manual inspection impossible at scale
Sophisticated Fraud
Fraudsters use increasingly convincing swaps
Impact: Human inspectors miss subtle differences
Speed Requirements
Returns must be processed quickly
Impact: Extended inspection delays refunds
Implementation Steps
Mixpeek compares returned item photos against original product images, detecting swaps, damage inconsistencies, and counterfeit indicators
Capture Return Images
Photograph returned items at receiving
const returnAnalysis = await client.returns.analyzeReturn({return_images: returnPhotos,order_id: orderId,product_id: productId});
Compare to Original Product
Match against catalog and order images
const comparison = await client.returns.compareToOriginal({return_analysis: returnAnalysis,original_product_images: catalogImages,order_photos: orderFulfillmentPhotos,checks: ['product_match', 'condition_match', 'counterfeit_indicators', 'damage_assessment']});
Generate Fraud Decision
Determine return acceptance
const decision = await client.returns.getDecision({comparison: comparison,customer_history: customerReturnHistory,policy: 'standard',threshold: 0.75});
Feature Extractors Used
Retriever Stages Used
Expected Outcomes
85% of fraudulent returns identified
Fraud Detection
60% reduction in return fraud losses
Loss Reduction
5x faster than manual inspection
Processing Speed
2% false positive rate (legitimate returns flagged)
False Positives
Legitimate returns processed faster
Customer Experience
Frequently Asked Questions
Related Resources
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Ready to Implement This Use Case?
Our team can help you get started with Visual Returns Fraud Prevention in your organization.