Fraud Detection with Graph Features and GNN

Description

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Identifying fraudulent behaviors is becoming increasingly more complex as technology advances and fraudsters constantly evolve new ways to exploit people, companies, and institutions. The complexity grows as companies introduce new channels, platforms, and devices for customers to engage with their brand, manage their accounts, and make transactions.

Graph neural networks (GNN) are increasingly being used to identify suspicious behavior. GNNs can combine graph structures, such as email accounts, addresses, phone numbers, and purchasing behavior to find meaningful patterns and enhance fraud detection.

Join TigerGraph’s Nikita Iserson to learn how graphs are used to uncover fraud.

Agenda:

• Introduction to TigerGraph
• Fraud Detection Challenges
• Graph Model, Data Exploration, and Investigation
• Visual Rules, Red Flags, and Feature Generation
• TigerGraph Machine Learning Workbench:
• XGBoost with Graph Features
• Graph Neural Network and Explainability

About Your Speaker

Nikita is a Senior ML/AI Architect at TigerGraph with over 10 years of experience in software engineering, data warehouse development, data analytics, and machine learning. He has built demand forecasting, network analysis, recommender systems, digital twins, and much more covering a wide range of industries, including telecom, retail, and banking.