The Challenge
The client was losing an estimated $200M annually to sophisticated fraud rings exploiting slow, rule-based detection that took 45 minutes per transaction. Their existing system generated 65% false positives, straining operations and frustrating legitimate customers.
Our Solution
We designed and deployed a real-time ML risk-scoring pipeline on AWS SageMaker, processing up to 2 million transactions daily across 12 regional markets. An ensemble model combining gradient boosting, neural networks, and graph-based analysis identifies fraud patterns invisible to rule engines. The system learns continuously using active learning loops.
Measurable Results
- 40% reduction in fraud losses ($80M saved annually)
- 0-second scoring latency (down from 45 min)
- False positive rate cut from 65% to 8%
- 12 markets live within the first 90 days
- 99.98% system uptime in production