Untangling the Dark Web of Business Ownership


It is becoming increasingly difficult for financial institutions to win what appears to be the never-ending war on financial crime. The incidences of money laundering, corruption continue to run out of control, and there is no need to look beyond the huge fines imposed on financial institutions (FIs) to find evidence.

One of the reasons banks and businesses are losing the battle is that it is also increasingly difficult for them to understand exactly who they are dealing with. Corporate ownership is often a complex web of anonymous subsidiaries and holding companies owned by shell companies with multiple shareholders whose identities can be almost impossible to unmask.

“It’s a multi-layered problem at its heart. First, we need to ask ourselves if the data is still available, ”said Farley Mesko, CEO of Sayari Labs. “Even if the information is available, there is no standardization in the way [it’s] structured or reported or the language in which it is written.

Find the data

Sayari Labs is a financial data intelligence startup whose mission is to unravel this web. The company, which raised $ 40 million in a Series C funding this month, aims to help financial institutions analyze the complexities of global business ownership and business relationships so that they can comply with Know Your Customer (KYC), Anti-Money Laundering (AML) and other regulations.

Read more: Sayari Labs raises $ 40 million to boost business transparency

Sayari gets her insight by browsing publicly available data archives and regulatory documents around the world to find out who is doing what, with whom, and why.

Mesko said the information Sayari provides is extremely valuable because even in developed markets like the UK it can be incredibly difficult to establish who is running a given entity.

“The UK is the perfect example because all the data is there, accessible to the public. They have had a publicly available beneficial ownership register for years now, ”he said. “But you could cite dozens of examples of companies whose owners of listed natural persons are a Chinese company, without any information on who actually owns them.”

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Financial institutions that do not know who they are dealing with can face severe penalties, potentially reaching millions, sometimes even billions of dollars. One of the most high-profile examples is French bank BNP Paribas, which in 2015 was fined a record $ 8.9 billion for violating sanctions that illegally opened up US financial markets to countries like Sudan. , Iran and Cuba.

Mesko said the fine was a wake-up call to the industry and banks responded by doing more to try to understand who they are doing business with. In particular, financial institutions take a closer look at the context in which a business or an individual related to a specific business operates.

“It’s not just about knowing your immediate customer, but understanding who they really belong to, who benefits from this business, wherever they are in the world and who else they might do business with,” did he declare.

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Dig deeper into the data

It’s a lot of work, but Mesko believes the financial institutions that are doing it are already seeing results.

“Those who have not done so are the ones receiving increasing fines,” he said. “A lot of this is because of not understanding your customer book. If you don’t understand your customer book, you can’t really assess your risk. So there is no way to design an effective risk-based approach.

Meanwhile, regulators like the Financial Crimes Enforcement Network (FinCEN) and the Wolfsberg Group are also taking the initiative to move away from current and somewhat inadequate anti-money laundering rules. Many executives today reward financial institutions that meet seemingly arbitrary criteria and tick specific boxes on checklists based on rigid IT logic. In the real world, these controls remain largely ineffective.

“The good news is that there is a collective action problem around this, implementing standards like the Global Legal Entity Identifier, but this process is only a few years old,” Mesko said. “They cover about 50 million stocks and companies, but we estimate that there are probably half a billion companies in the world.”

Further reading: Deep Dive: How AI and ML-Based Analytics Can Improve Fraud Detection and Investigation

Mesko wants to see a change in the way regulators look at financial institutions so they don’t just tick different boxes on a checklist.

“They have to come and ask questions such as what the institution is doing to measure and assess risk and what they are looking at internally,” he said. “They must examine elements such as the effectiveness of their engagement with the police, the usefulness of the information they provide them. Theoretically, this is how it should work, and it would be a huge change from the way things were in the past. “

Such a drastic regulatory change, combined with more actionable information like that provided by Sayari, will help prevent crime and create new business opportunities, Mesko believes. In recent years, there has been a trend of “de-risking” in the banking sector, with institutions avoiding certain regions and sectors of activity because the risk of fraud is deemed too high.

But Mesko said there are a lot of untapped opportunities in those parts of the world. Institutions just need a way to identify legitimate businesses.

“In a lot of these places that you think are high risk and low transparency, there is actually a wealth of information,” he said. “It might not be nice and well structured and readily available, but it’s there and it’s in the public domain.”



On: Eighty percent of consumers want to use non-traditional payment options like self-service, but only 35 percent were able to use them for their most recent purchases. Today’s Self-Service Shopping Journey, a PYMNTS and Toshiba Collaboration, analyzes more than 2,500 responses to find out how merchants can address availability and perception issues to meet demand for self-service kiosks.


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