2026 · Big Data · AI

Order Fraud Detection

Order Fraud Detection
Order Fraud Detection

Real-time fraud detection system protecting millions of orders from financial risk at scale

Tech Stack
PythonApache SparkCeleryOracle DBMongoDBOpenTelemetry
Overview

Order Fraud Detection is a large-scale data pipeline designed to identify fraudulent transactions across high-volume order streams in real time. The system processes millions of events per day using Apache Spark and Celery workers, applying rule-based scoring and behavioral pattern analysis to flag suspicious orders before fulfillment. OpenTelemetry provides full observability across the pipeline. Built for a warehouse and logistics operation handling nationwide distribution, it significantly reduced financial exposure from fraud while maintaining full throughput at production scale.

Goals
  • 01Detect and flag fraudulent orders in real time before they reach fulfillment
  • 02Process millions of daily transactions with sub-second detection latency
  • 03Reduce financial loss from order manipulation and identity fraud
  • 04Provide an auditable fraud event log for compliance and review teams
Outcomes
  • Processes 2M+ order events per day with sub-500ms detection latency
  • Reduced fraud-related financial loss by over 70% in the first quarter
  • Full audit trail enables compliance review and model retraining