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Machine Learning
FinTech Fraud Detection
99.8%
Accuracy
-40%
False Positives
<50ms
Processing Time
The Challenge
A leading neo-bank was facing a surge in sophisticated fraud attacks that traditional rule-based systems couldn't catch without blocking legitimate users.
The Solution
We engineered a real-time anomaly detection pipeline using an ensemble of Gradient Boosted Trees and Deep Learning autoencoders. The system analyzes 500+ features per transaction in milliseconds.
Tech Stack
PythonTensorFlowAWS SageMakerKafkaRedis