Fraud Detection Platform
Real-time transaction monitoring system processing over 500k transactions daily, using machine learning to detect fraudulent activity with 95% accuracy while maintaining a false positive rate below 1.5%.
The Challenge
Our fintech client was experiencing a surge in fraudulent transactions, with their legacy rule-based system struggling to keep pace with increasingly sophisticated fraud patterns. Key challenges included:
- High false positive rate (6%) causing legitimate customer transactions to be blocked
- Processing delays of 1-3 seconds impacting user experience
- Manual review backlog of 2,500+ flagged transactions
- Inability to detect emerging fraud patterns in real-time
Client Profile
- Industry
- Financial Services
- Company Size
- Series B Fintech Startup
- Transaction Volume
- 500k+ daily transactions
- Project Duration
- 12 weeks
- Team Size
- 4 engineers + 1 ML specialist
Our Solution
ML-Powered Detection
Built ensemble model combining Random Forest, XGBoost, and neural networks trained on 2+ years of historical transaction data with 180+ features.
Real-Time Processing
Event-driven architecture using Kafka for streaming and Redis for sub-300ms scoring, with automatic scaling to handle peak loads.
Adaptive Learning
Continuous model retraining pipeline that learns from analyst feedback and emerging fraud patterns, with automated A/B testing.
Technical Architecture
System Components
Data Pipeline
Kafka streaming for real-time ingestion, S3 data lake for historical analysis
ML Models
SageMaker for model training/deployment, MLflow for experiment tracking
Inference Engine
Lambda functions with Redis caching for sub-300ms response times
Security & Compliance
End-to-end encryption, PCI DSS compliance, audit logging
Monitoring
CloudWatch dashboards, PagerDuty alerts, model drift detection
Analytics Dashboard
React-based analyst portal for reviewing flagged transactions
Key Technologies
Results & Impact
Performance Improvements
Business Impact
"PrismLabs transformed our fraud detection capabilities. The system not only catches more fraud but does it faster and with fewer false positives. Our fraud analysts can now focus on truly suspicious cases instead of wading through false alarms."- VP of Engineering, Fintech Client
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