Just hearing the words “Corporate Structure” probably puts a lot of us to sleep. It sounds like one of those boring (sorry) topics that is the bread and butter of Lawyers (sorry, again!) but like raw eggs and kale for the rest of us. But what if we told you that we could give you a different, more interesting perspective? One that could potentially open your eyes into the several ways corporate structures (and the people behind them) reveal the reasons why they are built the way they are.
Interested? Great! We’ll try and give you such a perspective using Network Science, a less well known (but powerful) part of the Data Science world.
To do the magic, we need data on corporate structures, which basically means we need names of companies and their owners (both individual and companies that own other companies). We wanted the data to be real-world so that what we tell you is a reflection of reality. Fortunately, 81 countries around the world have what they call a “Beneficial Owner Database”, and the UK has one that provides the information publicly through Companies House. That’s where we got the data, which, after a bit of processing, looks something like this:
For privacy reasons, we’ve masked the company_number, which represents a company’s id with Companies House, with a 8 digit id known only to us. Owners of each of these companies are represented by the “unique_id” column, which is another 8 digit id we created using the name, birth date and nationality (in case of individual owners) or the Companies House Registration Number (in case of corporate owners). The “natures_of_control_val” column shows what percentage of the company is owned by the owners. In human terms, what the data essentially shows is:
Company b24d019 has one shareholder ig9hFxYx who owns an unknown percentage of shares in the company and Company a8680492 has two shareholders, 7BwacyDu and ozFTPYkj, who own 25-50% of shares each.
Once the data was processed, all we needed to do was to associate each company with its owners, including those where there is another company between and individual and a company. Confused? Maybe a graph will help…but before we show you the graph, you’ll have to keep in mind some shapes and colors:
Now look at this graph:
What the graph shows is that an an individual with id “V1AXyXip” owns 75-100% of a company 00002f71. That’s all you need to know.
The Average Joe
Why it is the way it is: Out of a sample of 400k companies, the above corporate structure is present in 81% of cases. This tracks with what we can infer about the real world: think about yourself, your friends and family members that own companies. It’s usually a few partners that own the company, right?
Below you have another structure from our real-world dataset. In this one, a single individual owns 154 companies.
Why it is the way it is: Now why would anyone go through the trouble of creating and managing a 154 companies. Just the administrative work around it would be a huge cost, right? Well if your bread and butter is to create and manage corporate structures, which is the case for [corporate] lawyers, then this sort of a structure is quite normal. They might create companies for their clients, or they might create what’s called a Special-Purpose-Vehicle (SVPs), which is a company made for a specific purpose (capn’ obvious here). That’s why we’re calling this type of a corporate structure the “lawyer”.
Funny thing is, we tracked the individual who owns the structure above and turns out he is, in-fact, a lawyer. A rather prolific one at that.
The Business Group
Moving onto more complex structures, here’s an example that we’re calling “The Business Group”. This type of structure has an added layer of complexity: that of of the Shareholding Company (or holding company), which is essentially a company that owns shares in another company (shown as yellow squares). However, even a Shareholding Company of a Shareholding Company of a Shareholding Company has to have individual shareholders at some level in the structure, which makes Network Analysis the perfect tool to reveal who the ultimate beneficiaries of any given company are.
In the example above, we can see that an individual with “LaDgbcC0” (left) owns a company with id “9bbec921” which owns 75-100% of a shareholding company with id “552d7580” with another individual with id “1bc3193f ” owning the remaining 25%. Company with id “552d7580”, in turns owns 25-50% shares in shareholding company “b6f007dc”, with another individual with id “mnMd659R” owning 25% and another sharholding company “fff0db27” owning the remaining 75%. The literal web of companies continues on and on and on, linking a total of 18 companies and 19 shareholders.
Why it is the way it is: On the positive side, as any business grows, it can take on new investor, venture out into new industries, spin-off existing departments and carry out numerous other corporate actions. On the negative, you often hear how companies set-up complex corporate structures to reduce tax liabilities and, in the worst of cases, to hide criminal activities. Both the positive and negative reasons contribute to a corporate structure where individuals, shareholding companies and companies creates a web that defines the modern business group.
The Business Group structure above is most likely made of people that know each other in one form or another. They have to, because they do business together. However, no one person in the above structure can be considered “powerful” based on the structure alone because there aren’t any individuals that are (literally) at the “center” of the network. The structure below is quite different:
What you see in the structure above is two distinct groups of individuals and companies (represented by large circles), who are connected to each other only through two individuals (stars in the left and right hand side in the center), whom we’re calling the bridge(s). These individuals hold a lot of power in this network as they (and through companies they own), are the only people that can make things happen between the two groups.
Why it is the way it is: Like you and I have a personal network of friends and family, we also have professional networks of colleagues and acquaintances. We are the bridge between our personal and professional networks. Similarly, in the corporate world, a few individuals might work with two groups of people on completely different ends of the spectrum: for example, a venture capitalist who invests across industries might setup two SPVs, one that invests in Pharmaceuticals and another in Tech Companies. In this case, the VC’s company, which owns shares in both SPVs would be the “bridge” that connects the two groups together.
So far i’ve explained to you why the structures are the way they are and what incentives participants within the structure have to build them that way. However, analysis of corporate network, financing networks and well, social networks too can also be powerful tools for data professionals:
- Financial/ Corporate crimes: If any one entity is suspected of being involved in financial crimes, say money laundering, knowing the network enables anti-money laundering and other financial crimes prevention data professionals to quickly identify the ultimate beneficiaries of the entire network. As another example, the position held by the “bridges” in the Bridge structure makes them ideal candidates to control the flow of money and information between the two groups. If businesses in both side of the structure are suspected of being involved in criminal activities, the best place to start looking for evidence might be with the “bridges”.
- Investment: Consider the Business Group structure above. Say a holding company X is publicly listed on the stock market and so are 3 of the companies it owns a 100% of. Now X is trading at a valuation of $100m, and each of the 3 companies it owns is trading at a valuation of $50 each. If you knew the corporate structure, it would be obvious that X’s shareholding of the 3 companies alone is worth $150m while X itself is trading at a valuation of $100m. In other words, if you buy shares in X, you’d be getting $150m worth of assets for a price of $100m. That’s quite the bargain!
Of course there are other use cases of an analysis such as the above. Feel free to share them with us in the comments. And to know more about what corporate structures can reveal to you…
Keep Data. Decisions. Repeat-ing,