Clandestine cut fraud false positives by 43% and slashed investigation time by implementing Source's adaptive machine learning system — moving from reactive rule-checking to intelligent, real-time risk assessment.
Introduction
Clandestine processes millions in cross-border transactions daily for digital-first businesses. Fast, secure, frictionless — that’s the promise. But as transaction volume exploded, their fraud detection system became a liability.
The problem wasn’t sophistication — they had robust security infrastructure. The problem was rigidity. Their rule-based fraud detection system flagged thousands of legitimate transactions daily, creating massive review queues while sophisticated fraud occasionally slipped through undetected. Analysts were burning out. Legitimate customers were getting frustrated. Growth was being throttled by a system designed for a fraction of their current scale.
Result
Three months in, the impact was undeniable:
- 43% reduction in false positive rates
- 60% faster case resolution
- 35% lower fraud prevention costs
- Higher customer satisfaction and transaction success rates
