In 2016 the UK government launched the world’s first “beneficial ownership” register; a requirement for all UK companies to register who controlled them. The register comprises of in excess of 4.5 million companies and 4 million individual people. We present how we built a graph database mapping the UK network of control structures using Python and a Neo4j graph database, and the first insights.
In June 2016 the UK government launched the world’s first “beneficial ownership” register; a requirement for all UK companies to register who were the “persons of significant control”, PSCs, who actually controlled the company. Recent investigative journalism has made headlines with the leaking of the Panama and Paradise papers and it is clear that transparency in corporate ownership needs to be a significant factor within modern democracy. In a partnership between DataKind UK and Global Witness we have built the worlds first network graph mapping all of the UK public data on those who control corporate interests in the UK; it comprises in excess of 4.5 million companies and 4 million individual people. It has been enriched with company officer data and metrics of financial secrecy based upon geographic regions.
The goal of the project was to enable Global Witness to search for "shady patterns" within corporate ownership networks to act as leads for investigative journalism to expose corrupt practices. Further more, we were able to analyse the completeness of the register and identify ways of improving such data structures to inform other world governments how to best build similar public registers of corporate ownership.
We present here how we built this amazing data structure using Python tools for cleaning and data processing and a Neo4j graph database storing the network graph itself. In addition, we share the first insights derived from this process.