Autonomous Fraud Detection System
SYSTEM VISUALIZATION
-40%
False Positives
<15ms
Detection Speed
$50M+
Fraud Prevented
99.9%
Automation Rate
The Challenge
Nebula Finance faced a critical issue: their rule-based fraud detection system was generating 40% false positives, freezing legitimate user accounts and causing massive customer churn. They needed a system that could learn evolving fraud patterns in real-time without human supervision.
The Solution
We engineered a hybrid architecture combining LSTM (Long Short-Term Memory) networks for sequence analysis with Transformer models for contextual understanding. The system processes transaction streams in real-time, flagging anomalies based on behavioral vectors rather than static rules.
Key Deliverables
- Architecture Blueprint
- Production Model Weights
- API Documentation
- Dashboard Interface
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