Finding Hidden Fraud Networks: Native Graph Analytics in Snowflake with Neo4j

Build smarter fraud detection into your Snowflake workflows—no data movement required. In this technical walkthrough, you’ll learn how to use Neo4j’s Graph Analytics natively on Snowflake to uncover hidden patterns in transactional data using advanced community detection algorithms.
With billions in transactions flowing through P2P networks, traditional tools fall short in surfacing complex relationships between users. Aura Graph Analytics offers a scalable, serverless solution to detect suspicious activity faster and more accurately.

What You’ll Learn:
– How to use Neo4j Graph Analytics natively inside Snowflake (no ETL, no pipelines)
– Architecture walkthrough: Graph projection from SQL tables, serverless compute, write-back
– Deep dive into the Louvain algorithm and why it’s ideal for fraud analytics
– How to structure Snowflake tables for graph operations

Explore Neo4j Aura Graph Analytics → https://bit.ly/43rPYNK
Get started with Neo4j AuraDB → https://bit.ly/451ssda
Access the code and dataset on GitHub → https://bit.ly/45xSTXY