65%
Fraud Reduction
50,000+
Transactions/sec
<100ms
Detection Latency
The Challenge
A fast-growing payments startup was experiencing rising fraud rates as transaction volume scaled, with their rule-based detection system unable to keep pace with evolving attack patterns.
Our Solution
We developed a real-time fraud detection pipeline using streaming data ingestion, an ensemble ML model trained on transaction behavior, and an automated retraining workflow triggered by model drift.
The Results
Fraud losses dropped 65% within the first quarter of deployment. The model processes over 50,000 transactions per second with sub-100ms latency.
Tech Stack
AWS SageMakerKinesisPythonMLflowPostgreSQL