FintechFraud Prevention & Security AI20235 months
Real-Time Transaction Fraud Detection
97.8%
Fraud Detection Rate
Recall on fraud transactions vs 72% baseline
84%
False Positive Reduction
Analyst alert volume reduced from 1,200 to 190/day
$38M
Fraud Losses Prevented
Annualized fraud loss prevention
<42ms
Decision Latency
P99 end-to-end transaction scoring latency
The Challenge
A payment processing startup handling $850M in monthly transaction volume was losing $4.2M monthly to sophisticated fraud rings that easily bypassed traditional rule-based systems. Alert fatigue had analysts reviewing 1,200+ false positives daily.
Our Solution
We deployed a graph neural network that maps transaction networks and identifies coordinated fraud patterns invisible to point-in-time models. Running on Apache Kafka with sub-50ms latency requirements, the system scores every transaction in real-time and uses adaptive feedback loops to evolve with new fraud patterns.
Key Outcomes
97.8%
Fraud Detection Rate
Recall on fraud transactions vs 72% baseline
84%
False Positive Reduction
Analyst alert volume reduced from 1,200 to 190/day
$38M
Fraud Losses Prevented
Annualized fraud loss prevention
<42ms
Decision Latency
P99 end-to-end transaction scoring latency
Project Details
ClientVeritas Payment Technologies
IndustryFintech
Duration5 months
Year2023
Tech Stack
PythonPyTorch GeometricApache KafkaApache FlinkNeo4jRedisGCPKubernetes
Tags
Anomaly DetectionStream ProcessingGraph Neural NetworksKafkaLow Latency
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