Building a Full-Stack Fraud Detection Solution with Kafka, GraphQL, and Neo4j Graph Algorithms

Graphs help drive financial fraud investigations, social media analyses, network & IT management use cases, recommendation engines, and knowledge management. These are all cases where patterns of interaction in your data (for example, a pattern of structured financial transactions) matter more than the individual data points (a single transfer). In a graph, we can enrich our social network streams with powerful graph algorithms that tell us about user and event influence through graph centrality, then stream results back to Kafka. Stream and table duality becomes the stream / table / graph trinity. We’ll cover how to easily transform Kafka streams or tables into graphs and query them declaratively using Cypher or GraphQL. We’ll demonstrate how it can be used to tackle social network analysis problems and discuss how the approach can be extended to real-time financial fraud detection and more.