Beneficial Ownership and Networks of Incorporation

5. How to document beneficial ownership 

A key issue for governments is how to gather information about all the various facets of beneficial ownership, and how to interpret it.

Disclosure Requirements

The first and most obvious method available to states is to attempt to impose disclosure requirements which would automatically provide a full picture. As we have seen, there are numerous and growing attempts to do this around the world, both generally and specifically in the extractive industries. The EITI standard, which is adhered to by 51 countries, will include a core requirement for companies to declare UBO by 2020. The EU Anti-Money Laundering Directive issued in mid-2015 imposes the same requirement for its 28 member states in all business sectors.

There are a host of recommendations for what such requirements could contain, from the FATF recommendations, to the host of other policy advice published by the OECD, the IMF, and others. In some cases, requirements could be built out at a regulatory level, based on legislation already in place to deal with corporate governance. In others, new laws would be required.

It is safe to assume that most such regulatory approaches involve a considerable investment of time to advocate, within and across government agencies, in parliament and other parts of the government, as well as the public. And then enforcement must also be considered, both in terms of the resources needed to do it, and the track record of the government, or agency, in question in terms of its ability to enforce existing laws and regulations.

Data Gathering

A parallel and complementary approach to beneficial ownership is to investigate what routes exist to gather data at a technical level. Broadly speaking, whether there are ways states can use corporate intelligence to increase knowledge about BO.

Closed source offerings

Corporate intelligence is a significant industry in its own right. Financial publishers such as Bloomberg, Thomson Reuters, Dow Jones, Lexis Nexis and others offer packages around due diligence. Much of the focus of these is on enabling companies to be certain they are meeting compliance in the major economies of the world, such as sanctions legislation in the USA or Europe, anti-money laundering provisions, anti-bribery legislation, and so on.

In addition, there are hundreds of corporate intelligence firms who offer bespoke services. For a fee, these companies will compile reports on individuals, companies or business operations.

A bespoke investigation will involve talking to contacts in the target business environment, seeking corporate intelligence in the field, and matching what informed parties there say against more formal information sources. Typically, the company then writes a report and submits it to the client, together with a face to face presentation.

It is important to understand there may be differences of context and norms between a company and a public body. Often the most valuable parts of corporate intelligence revolve around qualitative, and subjective, assessments of political risk, personal integrity, and opaque political power structures. In a company context, the managers who have commissioned such a report are generally free to act on it, subject only to general rules of corporate governance. There is no ethical or practical issue, in other words, with arbitrary decision making against informal evidence.

A government agency may find itself in a different situation. With limited resources, the decision to deploy a corporate intelligence firm to do due diligence, including beneficial ownership, on a business operation first needs to justify itself in cost terms. Corporate intelligence firms often charge several thousands of dollars a day, and a significant report could easily run to the tens of thousands of dollars. This might make it justifiable in a case where revenue flows at stake are clearly high, but less so for the “long tail” of smaller operations in a country’s oil or mining sector, for example.

Second, there is the question of availability of such corporate intelligence, and the profile an investigation itself would have. The corporate intelligence industry gravitates towards where the major interests of its core clients, the companies themselves, lie. Typically, they may have more direct contacts to run due diligence in large countries, such as the Middle Eastern oil economies, Nigeria, Brazil or Indonesia, than in smaller countries. Of course, there large extractives projects can turn up in otherwise small economies, such as the Simandou iron ore project in Guinea, for example, or Oyu Tolgoi in Mongolia, and corporate intelligence firms will respond to demand from business for due diligence services there. But in such cases, the risk is also increased that the investigation itself is harder to run discretely. Business and political circles in a country like Sierra Leone, for example, are small, and anyone seeking out and informally interviewing a number of players, including people personally connected to officials in government who have commissioned the report, may attract attention.

Thirdly, there is the question of the admissibility of such evidence, interpretation, and techniques for public policy making. Clearly there will be some governance structures, and cases, which can support the gathering of corporate intelligence in this way. But there may be others in which decisions based on such techniques would come under greater suspicion and scrutiny than they would in a company accountable only to its shareholders, with the unambiguous single goal of maximising shareholder revenues. How would public officials defend a decision made on confidential advice, for example (as such corporate intelligence reports frequently are)? How could data and intelligence gathered in this way be integrated, if at all, into formal public systems, and who would see it?

Open data approaches

A third and fast growing approach to gathering beneficial ownership data is the use of open data. This is growing because of the advancing norm of global transparency, represented in such initiatives as EITI, the Dodd-Frank legislation in the United States and recent directive in the EU, as well as the growth of open data techniques and technologies.

Companies themselves have been required to submit some form of reporting about their activities ever since the limited liability corporation was introduced in capitalist economies in the 19th century. Until recently such reports remained locked away in government offices, inaccessible to interested researchers. But company registries are increasingly coming online and providing documents in digital format, and the means to aggregate them are appearing.

For instance, as of November 2016, has details on 115 million companies in 100 jurisdictions around the world available for open view. The company aggregates all basic data provided online. Within the extractives sector, the Aleph search engine developed by OpenOil gathers in all filings made by extractive sector countries in the major financial markets of the world over the past few years and makes them word searchable. These instances are attempting to create global information sources from nationally filed documents, to align with the global nature of operations of the industries they cover.

One advantage of open data is that it is possible to “reconcile” data from one source with another, if they are in compatible technical formats. Increasingly such data sources are indeed aligned. So, for instance, a third party website can decide to set up company details from OpenCorporates, find all instances within Aleph of documents filed by those companies, and further, for example, to scrape all significant documents off the company’s own website, and put them together in one place, searchable. This can happen automatically, in the sense that once the computer programs have been written and put in place to create and integrate the information “feeds”, they do not have to be downloaded by human beings.

It is important to bear in mind, though, that such open data products are only as good as the information that went into the documents in the various different sources that they access. The “corporate record”, the sum of reporting by extractives companies, is vast – numbering tens of millions of documents. The challenge, though, is to find key information about ownership and other relations within such a huge body of documents. Also, the corporate record remains partial. Companies put large amounts of effort into meeting disclosure requirements in the major financial markets of the world, particularly in the jurisdictions where they are domiciled. But this does not necessarily mean that all significant details of their operations are published.

For instance, we have seen that in the realm of beneficial ownership it is important to establish the exact hierarchy of ownership through corporate chains – from an upstream entity in the operating environment in Africa, for example, to the headquarters in Canada or Australia. But companies routinely publish only partial lists of all their affiliates throughout the world, and even those lists often do not show the individual stages in the hierarchy, but instead just provide a “flat” list of subsidiaries.

This leaves the researcher with the knowledge that the companies in the list are related somehow – but without knowing exactly how. Similarly, some jurisdictions impose requirements on companies to publish what are called “related party transactions”. Some of these could relate to the transfer pricing issues discussed above. But the rules also allow a lot of consolidation, and exclusion on the grounds of non-materiality.


In short, beneficial ownership is both a complex and important issue, but there are significant limitations on each of the three techniques that governments can use to acquire information about it – introducing and enforcing disclosure requirements, using existing corporate investigation techniques, and deploying open data solutions.

It is likely, then, that many agencies should consider a mixture of all three methods, in sequence or in parallel, and set expectations accordingly.