The utility of beneficial ownership data is enhanced when the data is available in a structured format. This allows the data to be easily analysed and linked with other datasets, enhancing the data’s utility to expose transnational networks of illicit financial flows and support effective and timely due diligence. When beneficial ownership data is structured and interoperable it is also easier to verify, as a greater range of verification mechanisms can be used. The Beneficial Ownership Data Standard (BODS) is a template for publishing structured data about beneficial ownership in a format (JSON) that can be read and understood by computer systems around the world.
When data is machine readable and available in bulk, multiple disclosures can be analysed together. This allows users such as Financial Intelligence Units, banks, and journalists to apply data science and machine learning techniques to identify suspicious patterns of ownership or beneficial owners that appear on other datasets of interest (for example, sanctions lists). Where the private sector and civil society have access to beneficial ownership data in bulk, evidence shows that innovations can drive development of new due diligence products and identification of potential corruption cases.
Open data is digital “structured” or “machine-readable” data that is “made available with the technical and legal characteristics necessary for it to be freely used, reused, and redistributed by anyone, anytime, anywhere.” In other words, any user of open beneficial ownership data should be able to access the data, search it freely and/or download it as structured data – for example, a .csv file that can be imported into Excel – , and use it for any purpose.
Inherent in this definition is that the data should be accessible to the public, which we cover in another briefing (“The case for public beneficial ownership data”). Here, we briefly cover the benefits of publishing beneficial ownership as structured data.View document