Combining high-quality beneficial ownership data with other datasets can be crucial to help detect potential risks during customer due diligence, know your customer checks and sanctions screening processes.
Whether you are trying to detect the involvement of sanctioned individuals in ownership chains, or understand which individuals or corporate networks have been awarded a large number of public procurement contracts, spotting such risks relies on how you can best connect information across multiple datasets.
This proof-of-concept tool demonstrates the use of BODS data in a Resource Description Framework (RDF) format. RDF is a general framework for representing interconnected data on the web. This builds on previous work to create a BODS RDF vocabulary.
Open Ownership’s BODS data is converted into this linked data format. This way, the BODS data can be combined with public procurement data published in line with the Open Contracting Data Standard as well as with sanctions data published by OpenSanctions using the FollowTheMoney data model and the Offshore Leaks database from the International Consortium of Investigative Journalists.
The resulting data can then be queried using RDF/SPARQL (a query language used to express queries across diverse data sources) to leverage its graph nature for a series of risk and compliance use-cases. Both individuals and companies can be treated as targets.
For each of the use-cases explored below, we’ll explain how we’ve tackled the question, provide full technical notes, highlight discovery queries and showcase video demonstrations of our easy-to-understand, proof-of-concept demonstration tool:
1) Learning which companies have been awarded public contracts
By combining high-quality beneficial ownership and public procurement data, we can look for a target individual or entity and find any companies that they own or control (directly or indirectly) which have been awarded public contracts. This can bring greater visibility and transparency over larger corporate groups in terms of their reliance on public procurement.
2) Detecting sanctioned individuals or politically exposed persons
A common use-case in risk management is whether a target is subject to any sanctions, or if they are a politically exposed person (PEP). By leveraging the inherent graph nature of the BODS ownership model, we are able to push this one step further and identify what we called indirect risks: third parties connected to a target which are themselves sanctioned or count as a PEP.
3) Spotting risks associated with a registered address
The registered address for a legal entity can provide insights as to whether there is a network of companies related to a target. There are two areas which we focused on in this project in relation to registered addresses:
- Identifying sanctioned entities which share an address with a target (for which no direct risks are identified). This is using the OpenSanctions dataset.
- Identifying entities listed in the ICIJ Offshore Leaks database, which share an address with a target.
4) Exploring full beneficial ownership chains to learn about ultimate beneficial owners, parent companies and subsidiaries
Even without combining it with other datasets, high-quality beneficial ownership data can be used to build greater understanding of the connections and networks relating to individuals or companies. The BODS data model can be leveraged through RDF to enable several risk use-cases specific to ownership and control:
- tracing ownership chains to ultimate beneficial owners;
- identifying the ultimate parents of companies; and
- learning more about subsidiaries.
For those seeking to understand more about the technical approaches taken for this project, we have provided notes on data modelling and system design alongside instructions about running your own version of the application. You can also read more about the BODS RDF vocabulary on our public Github repository.
To share feedback about this project or to connect with the data support team at Open Ownership, please email [email protected].
Using reliable identifiers for corporate vehicles in beneficial ownership data
This technical guidance aims to help implementers and multi-stakeholder groups understand how reliable identifiers facilitate the connection of datasets such as disclosures on politically exposed persons, stock exchange filings and licence registers
Published: 23 October 2023
Open Ownership adds Legal Entity Identifiers to its datasets, partners with GLEIF
Global Legal Entity Identifier Foundation (GLEIF) and Open Ownership are collaborating, meaning that Legal Entity Identifiers (LEIs) are now being integrated into datasets produced in line with the Beneficial Ownership Data Standard (BODS) for the first time
Published: 27 September 2023