Follow the Money – Analyzing the FinCEN Files with Graph Data Science

The ICIJ recently published a big investigation of 83 journalists covering 2 trillion dollars of money flowing through big banks which were filed as Suspicious Activity Reports with the US Treasury, using Neo4j besides other tools. Some of the data was published, so that we can start looking at the players and transactions using a graph database, but even more interesting is applying graph data science to the dataset. With graph algorithms and machine learning (embeddings) we want to see if we can read between the lines of the raw data. Join me for this interesting investigative journey.

Free e-Book: Graph Data Science for Dummies: https://neo4j.com/graph-data-science-for-dummies/

FinCEN Files Blog Post https://neo4j.com/blog/analyzing-fincen-files-data-neo4j/

FinCEN Files Sandbox https://sandbox.neo4j.com?usecase=fincen

Graph Data Science Library 1.4 with Node Embeddings and KNN

https://neo4j.com/blog/announcing-graph-native-machine-learning-in-neo4j/">Announcing: Graph-Native Machine Learning in Neo4j!

https://neo4j.com/graph-data-science-library/

Concepts and Documentation
https://neo4j.com/developer/graph-data-science/graph-embeddings/
https://neo4j.com/docs/graph-data-science/1.4-preview/algorithms/fastrp/