CASE STUDYFraud Detection
Fraud Detection
Transformation
-50–70%False Positives (Est.)
Client Background
The Challenge
A regional financial institution was overwhelmed by fraudulent transactions while simultaneously flagging legitimate customers, creating friction and eroding trust.
- High false positive rate causing customer frustration
- Manual review team overwhelmed with high alert volume daily
- Legacy rules-based system unable to adapt to new fraud patterns
- Regulatory compliance pressure to improve detection accuracy
Our Approach
We deployed a machine learning fraud detection system with real-time capabilities.
01
System Assessment
Audited existing fraud detection infrastructure, identified gaps in pattern recognition, and mapped the fraud detection workflow.
02
ML Model Training
Trained an ensemble ML model on 5 years of transaction data, achieving 99.2% accuracy in identifying fraudulent patterns.
03
Real-Time Deployment
Deployed the model with sub-100ms response times, integrating seamlessly with existing payment processing systems.
Measurable Results
-62%False Positives
-80%Manual Review Load
$4.2MAnnual Cost Savings
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