OpenCamps - Analyzing the Paradise Papers - nov 2017
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The document summarizes an analysis of the Paradise Papers leak which revealed offshore holdings of politicians and public figures. A team of 380 journalists from 90 media outlets analyzed data on 120 politicians, including 14 country leaders, and visualized the financial connections between individuals, banks, companies and properties using property graph databases and technologies like Neo4j and BigQuery. The visualization helped uncover complex webs of offshore holdings among political and business elites.
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OpenCamps - Analyzing the Paradise Papers - nov 2017
- 1. Fighting Wealth Inequality Through Graphs: Analyzing the Paradise Papers
- 2. ABOUT ME • Developed web apps for 5 years including e-commerce, business workflow, more. • Worked at Google for 8 years on Google Apps, Cloud Platform • Technologies: Python, Java, BigQuery, Oracle, MySQL, OAuth ryan@neo4j.com @ryguyrg
- 9. The Data
- 11. 120 Politicians 14 country leaders Analyzed by: 380 journalists 90 media outlets
- 12. The Technology
- 19. Bank US Account 123 Person A Acme Inc Bank Bahamas Address X Person B
- 21. Bank US Account 123 Person A Acme Inc Bank Bahamas Address XNODE RELATIONSHIP Person B
- 23. Demo Viz
- 25. Demo Browser / Sandbox
- 27. Anti Money Laundering Anti Money Laundering
Editor's Notes
- “2.6 TB of leaked, that supposedly contained data detailing accounts and activities of the powerful and the wealthy for legal tax planning, but possibly also for illegal tax evasion.” “So they got this 2.6 TB huge data dump of spaghetti information and they wanted to make sense of that. They ran it through an open source pipeline of technologies and ended up with”<click>”11 MILLION documents, which btw is the largest leak in journalistic history. In these documents are emails, bank accounts, names, addresses etc, and they have to make sense of all that and uncover any newsworthy stories.” “Now let’s take a step back from data and technology and just think about what investigative journalism is. IJ is all about finding patterns. Here’s an example of a pattern:”<click> Person has Account with Bank. Yadayada, nothing wrong. Blabla lives on address.
- “We have an exciting day ahead of us. Let me take this first hour to take a step back and talk a little about the state of the graph space today, and much more importantly talk about where I believe the space is going. “It’s been a year since we had a GraphConnect here in the US, and what a year it has been. Graphs have had an impact on an order that we’ve never seen before. Let me give you a couple of examples.”
- “The first story is about the Panama Papers, which was the biggest news story of 2016, but its impact is still very live: a couple of months ago the prime minister of Pakistan resigned over findings in the Panama Papers, and just last week he was actually formally indicted for corruption.” “In this particular story, the heroes are two journalists at the Suddeutsche Zeitung who were provided with a”<click>
- “a million nodes, or 11.5 million nodes, or a billion nodes, or 100 billion nodes.”
- And finding money launderers