Back Market's fraud team builds AI detection system in one week, contributing to €1.2M savings initiative
Back Market faced persistent logistics fraud where fraudsters purchased high-value electronics, requested refunds, and returned empty boxes or manipulated shipping labels. The investigation process took several hours to days, advanced capabilities required engineering resources, and an estimated 0.3% of all parcels were potential fraud cases representing significant GMV loss.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Fraud claim submitted
A customer refund or claims request initiates the fraud investigation process.
Tools used
DustConfluence
Outcome
The AI-powered fraud detection system contributed to a fraud prevention initiative projected to save more than €1.2 million annually, with AI claims analysis alone preventing nearly €100,000 in fraud over five months. The fraud team can now adapt to new fraud tactics in less than one day and operates fully autonomously without engineering resources.
What failed first
A previous complex refund verification process triggered public backlash as customers complained about difficulty getting legitimate refunds. Manual SQL-based investigations could not scale, and SQL was ineffective for conversation pattern matching.