AI / Machine Learning / nn-15
Graph Attention Fraud Scorer
Converts enterprise data into faster decisions, measurable quality gains, and defensible automation.
Commercial Scale
$37,800 USD
Risk Reduced
Automation Reliability
Executive Situation
Payment risk models missed coordinated fraud patterns because individual transaction features could not expose shared devices, addresses, and account rings.
Modular Solutions Response
We modeled entities as a transaction graph and trained graph attention layers to surface suspicious neighborhoods. Streaming updates refresh local subgraphs, and investigators receive ranked evidence paths rather than opaque risk numbers.
Industry
Manufacturing
Category
Neural Networks & Deep Learning
Specialty
Production
Evidence Basis
Model + MLOps
parameters
58M GAT scorer
latency
52ms transaction score
training
103 GPU hours
loss
L = focal(y,ลท) + 0.1 graph_smoothness
Enterprise Security Gate
Network Access Restricted.
Detailed files, client-specific assumptions, and delivery channels remain controlled.