Exploring Fraud Detection With Neo4j & Graph Data Science  –  Summary

In the realm of data science and machine learning, fraud detection remains a significant challenge due to the entities trying to prevent detection. However, graph-based approaches like Neo4j Graph Data Science can model relationships between entities, providing a powerful tool for rapidly exploring, analyzing, resolving, and predicting fraud entities and patterns. By applying these methods to an anonymized data sample from a peer-to-peer payment platform, it is possible to identify new fraud risks that went undetected with non-graph methods, increasing the number of flagged users by 87.5 percent. Furthermore, this approach can be highly scalable and transferable to various fraud detection use cases, enabling practitioners to build more accurate and sophisticated fraud detection applications.