Whether you’re responsible for combating financial crimes, online identity theft, or trafficking operations, you know that early fraud detection is essential. For businesses to avoid the risk of losing money to fraudulent schemes, data scientists must be proactive in analyzing and predicting fraud patterns in their data. 

Join Nick Johnson, Product Marketing Manager for Graph Data Science, for Smarter Fraud Detection With Graph Data Science as he shows you how graph-based approaches empower data scientists to more easily identify fraudulent actors, phone numbers, IP addresses, and more.

In this 20-minute webinar, you’ll learn: 

  • What graph data science is
  • Why graph data science is better at solving your most challenging data problems
  • How data scientists traditionally identify fraud rings and suspicious actors
  • How to visually map and identify fraudulent actors and accounts using Neo4j Graph Data Science for fraud detection

Nick Johnson
Product Marketing Manager, Graph Data Science

Nick Johnson is a Product Marketing Manager for Graph Data Science at Neo4j, where he works to bring Graph Data Science to life with highly targeted messaging, positioning, case studies, content, and more. Prior to Neo4j, Nick worked in Product Marketing across several organizations including Microsoft, SAS, and Orbit. Nick holds a Master’s degree in International Development from Sciences Po and Bachelor degrees in International Studies and Swedish from the University of Washington.

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