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TIGERGRAPH Savanna
Overview
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Pricing
USER TOOLS
GraphStudio
Insights
Application Workbenches
Connectors and Drivers
openCypher Support
TIGERGRAPH DB
Overview
GSQL Query Language
Compare Editions
GRAPH DATA SCIENCE
Graph Data Science Library
Machine Learning Workbench
Solutions
Solutions
Solutions Overview
Increase revenue
Customer Journey/360
Entity Resolution
Product Marketing
Recommendation Engine
Manage risk
Fraud Detection
Threat Detection
Anti-Money Laundering
Risk Monitoring
Improve operations
Supply Chain Analysis
Network Optimization
Energy Management System
By industry
Advertising, Media and Entertainment
Financial Services
Healthcare & Life Sciences
Foundational
AI & Machine Learning
Time Series Analysis
Geospatial Analysis
Developers and Data Scientists
Download
TigerGraph DB
TigerGraph Docker Image
GSQL Client
Developers
Documentation
Developer Hub
Ecosystem
Community Forum
Resources
Blog
TigerGraph Blog
Resources
Demos
Webinars & Events
Videos & Podcasts
Benchmarks
Reports & Whitepapers
Datasheets
Services and Support
Services and Support
TigerGraph University
Training & Certifications
TigerGraph Support
Documentation
Production Guidelines
Company
Company
Company Overview
Leadership
Legal Terms
Patents
Security and Compliance
Careers
Join Us
Open Positions
Customers
Ford
Intuit
JPMorgan Chase
Jaguar Land Rover
Xbox
Read more success stories
Press Room
News and Press Releases
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TigerGraph Partners
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Category:
Fraud / Anti-Money Laundering
Using Graph Machine Learning to Improve Fraud Detection Rates
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Understanding Bank Fraud ~ by Harry Powell
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How Is Graph Database Used to Combat Money Laundering? ~ by Mingxi Wu, CEO, TigerGraph
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