The Problem We Were Brought In To Solve
Three Global banks losing $19M annually to CNP fraud and synthetic identity attacks undetected by rule-based systems — CBUAE requiring enhanced transaction monitoring.
How Revino Delivered
Federated ML consortium model with real-time scoring at <20ms, behavioural biometrics, and graph neural network for mule account detection.
Measurable Business Impact
✓Combined fraud losses: -43% ($19M→$10.8M)
✓$8.2M recovered through fraud reversal programmes
✓False decline rate: 2.8%→1.06%
✓840 mule accounts flagged in Month 1
✓CBUAE transaction monitoring compliance achieved
PythonLightGBMGraph Neural NetworksKafkaAWSCBUAE APIs
KEY METRICS
43%
Fraud reduction
$8.2M
Recovered
62%
Fewer false declines
<20ms
Scoring latency
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