RSA Detector
Backend Service
Project Summary
A reseller abuse prevention system for e-commerce platforms that detects unauthorized resale activities through address clustering, bulk purchase analysis, and order pattern recognition. Implements automated purchase limits and manual review workflows.
The system analyzes purchasing patterns, shipping addresses, and order histories to identify potential reseller activities using algorithms. It can detect bulk purchases, address clustering, and unusual order patterns in real-time.
Key features include automated intervention capabilities that flag suspicious orders, implement purchase limits, and trigger manual review processes. The system provides tools for managing reseller relationships and setting custom rules for different product categories.
Built with scalability in mind, RSA includes APIs for integration with other fraud prevention systems and generates detailed reports on reseller activities to help businesses maintain fair access to products while preventing unauthorized resale.
Case Study
Overview
Built a reseller-abuse detection service that scores orders in real time, applies purchase limits, and routes edge cases to manual review to protect inventory and ensure fair access.
Problem
Limited-quantity drops were being drained by resellers using bulk orders and address clustering. Manual review was slow, rules were inconsistent across teams, and abuse signals lived in separate systems.
Goals
- Score orders in under 2 seconds at checkout time.
- Detect clustered addresses and bulk-buy behavior with >90% recall.
- Keep false positives under 2% for legitimate buyers.
- Provide configurable rules per product category and launch.
- Expose an API for integration with existing fraud tooling.
Approach
- Combined rule-based checks with a weighted scoring model to balance speed and explainability.
- Used Elasticsearch for fast pattern searches across historical orders and Redis for rate limiting and hot signals.
- Kept PostgreSQL as the source of truth for review decisions and audit trails.
- Built a manual review queue with reason codes to tune thresholds post-launch.
Solution
A backend service with real-time scoring, address clustering, bulk-order detection, automated limits, and a review workflow, plus APIs and reporting for reseller activity.
Outcomes
- Reduced reseller take-rate on limited drops by ~25-35% within the first two launches.
- Cut review turnaround from hours to ~20 minutes with a prioritized queue.
- Improved inventory availability for genuine customers without blocking high-value orders.
Key Metrics
Timeline
Challenges
- Distinguishing legitimate bulk purchases from reseller behavior.
- Keeping decisions fast without sacrificing explainability.
- Reducing false positives while still stopping abuse.