A leading global payment processor was losing millions annually to fraudulent transactions. They needed a real-time system to detect and prevent fraud without impacting legitimate transactions.
The Challenge
The client was operating with rule-based fraud detection yielding only 60% accuracy and high false positive rates blocking legitimate transactions. Their legacy system couldn't scale to billions of daily transactions.
Our Solution
- •Conducted comprehensive audit of transaction data and fraud patterns
- •Built ensemble machine learning model combining gradient boosting and neural networks
- •Implemented real-time inference pipeline processing 100k+ transactions/second
- •Created feedback loop automatically retraining models weekly
- •Deployed on Kubernetes with 99.99% uptime SLA
Key Outcomes
- ✓85% reduction in fraud losses ($12M annual savings)
- ✓0.1% false positive rate (previously 15%)
- ✓Processed 50B+ transactions with < 100ms latency
- ✓40% increase in legitimate transaction approval
Technologies Used
Results
The AI-powered system immediately reduced fraud loss by 85%, enabling the client to expand to new markets with confidence. The system continues learning and improving, now handling billions of transactions annually.
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