2026 · Big Data · AI
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

