Fraud and bad orders create a double loss: chargebacks and manual review drag, plus slower fulfillment for clean orders. This case study summarizes a practical, production-ready order risk rules engine that screens orders in real time and routes only the right edge cases to review.
Written by Alexander Granovskiy - E-commerce Manager (Cleveland, Ohio, United States).
Purpose
This is a practical case study from my e-commerce operations work: a risk rules engine that screens orders in real time to reduce fraud and keep fulfillment fast.
What it does
Pre-capture screening
IP geo and proxy checks
Risk lists
Payment pattern analysis
Address validation (PO box, freight forwarder)
ERP and fraud API signals
Auto-release safe orders; route risky orders to review
Benefits
Fewer chargebacks and false declines
Faster clean-order flow
Results
+2 to +4 percentage points approval rate
-15 to -30% chargebacks
-10 to -20% false declines
Scope
Owned rules, integrations, playbooks, dashboards, and weekly reviews
Links
Full case study: https://www.alexgranovskiy.com/case-study-order-risk-rules-engine/
More case studies: https://www.alexgranovskiy.com/tag/case-studies/
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