Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, fraudsters today have sophisticated ways to get away with using collaborative efforts and synthetic identities. To uncover such fraud rings, it is essential to look beyond individual data points to the relationships between them.

Graph data science harnesses the power of connections to analyze data relationships, detect suspicious patterns and prevent fraudulent transactions.

Join us for this webinar on August 27 at 1:00 p.m. AEST for a deep dive into graph analytics for fraud detection where we will discuss:

  • How to use graph data science to prevent fraud
  • How to improve fraud detection with graph feature engineering
  • How graph analytics benefit even non-technical fraud investigators
Peter Philipp
Sales Lead, Australia - Neo4j

Peter Philipp leads Australia and New Zealand sales for Neo4j. Peter is focused on helping organizations benefit from graph technology, AI/machine learning and NLP. He has worked with government institutions and 5 of the world’s top 10 investment banks.

Dave Voutila
Solutions Engineer, Neo4j

Dave is a transplant to the state of Vermont, having grown up and spent most of his life in Massachusetts. After receiving his degree in mathematics, he worked in a variety of sales engineering roles at software startups and spent some time as an independent consultant. Prior to joining the Neo4j team, Dave was VP of Solutions for a SaaS startup where he was a full-stack engineer as well as the lead cloud systems engineer.

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