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AI Fraud & AML Summit London 2021 – Graph Analytics for Fraud Detection
Traditional fraud prevention measures tend to be very event-driven and focus on discrete data points such as specific accounts, individuals, devices, or IP addresses. However, fraudsters today have sophisticated ways to avoid traditional detection methods, using collaborative efforts and synthetic identities to commit fraud that flies under the radar. 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. In this talk, we’ll take a closer look at how your data science and fraud investigation teams can tap into the power of graph technology for higher quality predictions in detecting fraudulent activity and sophisticated fraud rings. This talk will explain and demonstrate how the power of graph databases and graph analytics can be used by a wide variety of organizations to extend their fraud detection arsenal, reduce false positives, and keep up with the ever-growing and evolving fraud threat landscape.
Specifically, this talk will focus on :
- An introduction to Neo4j and our graph platform
- An overview of the Neo4j graph database model
- A deep dive into how graph databases can be applied across industries and use cases to detect and prevent fraud
- A live demonstration of several fraud detection scenarios leveraging a graph database, advanced graph analytics techniques, and graph data visualization.