Real-Time Fraud Detection System
Developed an ML-powered fraud detection system for a digital payments company that reduced false positives by 60% while catching 40% more fraud.
Key Results
The Challenge
A fast-growing digital payments company processing $3B in annual transactions was losing ground to fraudsters. Their rules-based fraud detection system was generating too many false positives, frustrating legitimate customers while sophisticated fraud patterns slipped through.
Key Pain Points
- 15% of legitimate transactions incorrectly flagged as fraud
- Manual review backlog of 10,000+ transactions daily
- Average fraud detection taking 24+ hours, enabling "smash and grab" attacks
- Unable to meet PCI DSS requirements for real-time monitoring
Our Approach
We built a next-generation fraud detection system that combines machine learning with explainable decision-making, operating in real-time at scale.
Phase 1: Data Infrastructure
- Architected a streaming data platform capable of processing 50,000 transactions per second
- Built a feature store with 500+ engineered features from transaction history
- Implemented device fingerprinting and behavioral biometrics integration
- Created a fraud labeling pipeline with human-in-the-loop feedback
Phase 2: ML Development
- Developed an ensemble model combining gradient boosting and neural networks
- Implemented graph neural networks to detect organized fraud rings
- Built anomaly detection models for identifying novel fraud patterns
- Created an explainability layer using SHAP values for compliance
Phase 3: Production System
- Deployed models with sub-100ms inference latency
- Built a real-time scoring API with 99.99% availability SLA
- Implemented A/B testing infrastructure for model improvements
- Created analyst workbench for fraud investigation
Technical Implementation
Architecture Highlights
- Streaming: Apache Kafka + Flink for real-time feature computation
- Feature Store: Feast with Redis for online serving, Snowflake for offline
- ML Serving: TensorFlow Serving with Kubernetes autoscaling
- Graph Analysis: Neo4j for entity resolution and link analysis
- Observability: Datadog for model monitoring, custom drift detection
Compliance Integration
- PCI DSS compliant architecture with network segmentation
- SOC 2 Type II audit passed within 6 months of deployment
- Full decision audit trail for regulatory inquiries
- GDPR-compliant data retention and deletion workflows
Results
The system launched after 6 months of development, transforming fraud operations:
- 60% reduction in false positives, improving customer experience
- 40% increase in fraud caught before transaction completion
- <100ms latency for fraud decisions, enabling real-time blocking
- $8M annual savings from reduced fraud losses and operational efficiency
Client Testimonial
"The ROI was clear within the first quarter. We're catching sophisticated fraud patterns we never could before, and our customers aren't getting blocked for legitimate purchases anymore."
— VP of Risk Management