Who Owns What: Investigating Beneficial Ownership Using Knowledge Graphs

The term beneficial ownership refers to the individuals who control legal entities, such as companies, trusts, and foundations, even though they may not be listed directly as owners. This concept promotes transparency into corporate structures: for example, a beneficial owner may orchestrate several shell companies within a money laundering scheme while obscuring their own identity to avoid prosecution.

We want to be able to see through these schemes. Determining ultimate beneficial ownership (UBO) can be complex, especially when nominees or trusts are involved, or when datasets lack verified identifiers. While jurisdictions worldwide vary in how they collect and share beneficial ownership data, identifying UBOs is vital for countering money laundering, tax evasion, political bribery and so on. There are important needs for entity resolution in these use cases, especially when graph technologies are involved.

Stephen Abbott Pugh was the CTO at Open Ownership and the product owner for the Beneficial Ownership Data Standard (BODS), which provides a structured, interoperable format for representing UBO information. Stephen now leads a consultancy called Understand Beneficial Ownership and is well known for thought leadership and technical guidance on frontier issues relating to beneficial ownership transparency around the world.

These crucial data resources play central roles in the construction of knowledge graphs used for financial crime detection, OSINT, investigative journalism and related investigations.

In this live podcast with Stephen, we will discuss the challenges and opportunities in working with beneficial ownership data and understanding its uses across the world—especially in the context of graph technologies—and the initiatives driving toward standardization.