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?

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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.

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In this brief paper, you will:

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

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