Refund Abuse Detection
DTC Apparel Brand
Example scenario — illustrative metrics, not a guarantee
Scenario
A high-growth apparel brand processing 18,000+ returns per month was experiencing serial refund abuse — wardrobing, friendly fraud, and repeat offenders — that manual review teams could not detect at scale.
Approach
Configured policy-driven risk scoring across full customer transaction history. Cross-referenced return frequency, value patterns, and item categories over 90-day windows. The policy engine auto-flags high-risk cases and routes them to a dedicated review queue with full context.
Outcome
12% of monthly cases were flagged for human review based on policy-defined risk thresholds. Pattern detection covered 90-day customer windows with configurable risk thresholds per product category. Full audit trail maintained for every scoring decision.