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Deep Dive: Graph Analytics for Fraud Detection
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 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
Presenters:
– Amy Hodler, Graph Analytics & AI Program Director, Neo4j
– Dave Voutila, Solutions Engineer, Neo4j
Graph data science harnesses the power of connections to analyze data relationships, detect suspicious patterns and prevent fraudulent transactions.
Join us for this webinar 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
Presenters:
– Amy Hodler, Graph Analytics & AI Program Director, Neo4j
– Dave Voutila, Solutions Engineer, Neo4j