Clandestine
Year:
2025
Service:
AI-Powered Fraud Detection
Industry:
Banking
Size:
100-120 employees
Client Website:
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.
Challenge
Clandestine's fraud system operated on static rules: if transaction amount > X from country Y, flag for review. Simple. Predictable. And completely overwhelmed.
The consequences:
Drowning in false positives: Legitimate transactions sat in review queues for hours while analysts manually verified obvious approvals
Inconsistent decisions: Different regional teams applied rules differently, creating compliance risk
Reactive posture: The system could only catch known fraud patterns, not emerging threats
Customer friction: Genuine users experienced delays, declined transactions, and frustration
Every transaction review cost time and money. Analysts were spending 80% of their day clearing false alarms, leaving limited bandwidth for actual investigations. The system wasn't just inefficient — it was unsustainable.
Solution
Source rebuilt Clandestine's fraud detection from the ground up using adaptive machine learning that evolved with emerging threats.
Instead of static rules, the new system:
Learned continuously from transaction patterns, user behavior, and analyst decisions
Scored risk dynamically using hundreds of behavioral signals beyond simple threshold checks
Prioritized intelligently — surfacing only high-confidence anomalies for human review
Created feedback loops where every analyst decision improved future accuracy
The transformation wasn't just technical. Source integrated the system directly into existing workflows, so analysts got clearer context, faster access to relevant data, and confidence in the AI's recommendations. The machine handled volume; humans handled judgment.
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
But the bigger win was strategic. Clandestine's fraud team shifted from firefighting to pattern recognition — identifying emerging threats before they scaled, tuning the system proactively, and finally operating ahead of the curve. Fraud prevention transformed from a cost center into a competitive advantage.
