Graph Data Science Use Cases: Fraud and Anomaly Detection

Fraud is a financial drain, a risk for businesses and consumers alike. With fraud attempts skyrocketing, how can you identify fraud in time to stop it?


Graph-based approaches to detecting fraud analyze complex linkages between people, transactions, and institutions. Neo4j Graph Data Science effectively reveals patterns of fraud and surfaces anomalies.


In this brief paper, you will:


  • Learn three flexible techniques for detecting shifting fraud patterns
  • See a sample graph data model
  • Find out which graph algorithms to run – and why
  • Discover how a top fintech company reduces manual investigation and finds more fraud

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