Case study
Streamline Payments processes transactions for thousands of small businesses. When they came to us, their fraud detection was embarrassingly bad—false positives were killing merchant relationships, and real fraud was slipping through.
False Positive Rate
0.8%
Better Detection
4x
Annual Volume
$2B+
The challenge
Their legacy system flagged 15% of legitimate transactions as suspicious while missing actual fraud. Merchants were leaving. The team had tried to fix it internally but the codebase was a mess of spaghetti code from three different contractors.
Our solution
We rebuilt their fraud detection from scratch using ML models trained on their historical data. But the real win was the architecture—we designed it so their in-house team could tune the models without needing data scientists.
Results
False positives dropped from 15% to 0.8%
Actual fraud detection improved 4x
Merchant churn cut in half
System handles 3x the transaction volume
Technology stack
Frontend
Backend
Cloud
Other
