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). We’ll cover how to easily transform Kafka streams or tables into graphs, and query them declaratively using Cypher or GraphQL.

In graph shape, we can enrich our social network streams with powerful graph algorithms that tell us about user and event influence through graph centrality, then streaming results back to Kafka.

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.