Finding values from connected data: a case study from Paradise Papers

(Nov. 2017, https://neo4j.com/blog/neo4j-power-behind-paradise-papers/)

Using Neo4j, the ICIJ has built upon their Pulitzer Prize-winning investigation of 2016 – the Panama Papers – and after a year or so, they’ve released Paradise Paper. (More readings: https://neo4j.com/blog/analyzing-panama-papers-neo4j)

Similar to the Panama Papers before, Paradise Papers have revealed from those records that a large number of people and organizations use shell companies in tax havens and offshore jurisdictions to hide, move and spend huge amounts of money (billions to trillions of USD) without the necessary fiscal oversight.

Paradise Papers are built on top of new 1.4 TB of data – 13.4 million documents – includes information leaked from trust company Asiaciti and from Appleby, a 100-year-old offshore law firm specializing in tax havens as well as information leaked. The files were obtained by German newspaper Süddeutsche Zeitung and shared with Washington D.C.-headquartered ICIJ, a network of independent reporting teams around the world.

As in previous investigations, Neo4j plays a key role in revealing the connections between the wealthy, their money and the taxation-friendly countries in which it resides.

The reason: Graph databases excel at managing highly connected data and complex queries.

Instead of using tables the way a relational database does, graphs use special structures incorporating nodes, properties and relationships to define and store data, making them highly proficient at analyzing the relationships and any interconnections between data — and allowing journalists to “follow the money” easier than ever.

During the meetup, we will have a close look at the data model, the Cypher query and algorithms that enabled all of these behind the scene.

(graphs) -[:are]-> (everywhere)