It is getting harder for financial institutions to win what seems to be the endless war on financial crime. Incidences of money laundering, corruption continue to go unchecked, and one needs to look no further than the huge fines financial institutions (FIs) are hit with to find proof.
One of the reasons banks and corporations are losing the battle is that it’s also getting harder for them to understand exactly who they’re dealing with. Corporate ownership is often an intricate web of faceless subsidiaries and holding companies owned by shell firms with multiple shareholders whose identities can be almost impossible to unmask.
“It’s a multilayered problem at its heart. First and foremost, we need to ask if the data is even available,” said Sayari Labs Chief Executive Farley Mesko. “Even if the information is available, there’s no standardization in the way [it’s] structured or reported or the language it’s written in.”
Finding the Data
Sayari Labs is a financial data intelligence startup that has set itself the mission of untangling that web. The company, which raised $40 million in a Series C round of funding this month, aims to help financial institutions parse the complexities of global corporate ownership and commercial relationships so they can comply with know your customer (KYC), anti-money laundering (AML) and other regulations.
Read more: Sayari Labs Raises $40M to Boost Trade Transparency
Sayari obtains its intelligence by scouring the world’s publicly available data archives and regulatory filings for insights into who is doing what, with whom, and why.
Mesko said the intelligence Sayari provides is extremely valuable because even in developed markets like the U.K., it can be incredibly difficult to establish who is running a given entity.
“The U.K. is the perfect example because the data is all there, publicly available. They’ve had a publicly available beneficial ownership registry for years now,” he said. “But you could point to dozens of examples of companies whose listed natural person owners are a Chinese company, with no information about who really owns them.”
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Financial institutions that don’t know whom they’re dealing with can be hit with severe penalties, running into millions, sometimes even billions of dollars. One of the highest-profile examples was the French bank BNP Paribas, which in 2015 was slapped with a record $8.9 billion fine for sanctions violations that unlawfully opened the U.S. financial markets to countries such as Sudan, Iran and Cuba.
Mesko said that fine was a wake-up call to the industry and that banks have responded by doing more to try to understand whom they are doing business with. In particular, financial institutions are looking much more closely at the context in which a business or individual tied to a specific business operates.
“It’s not just knowing your immediate customer, but understanding who really owns them, who benefits from that company, wherever in the world they might be and who else they might be doing business with,” he said.
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Digging Deeper into Data
It’s a lot of work, but Mesko believes that those financial institutions that do this are already seeing results.
“The ones who haven’t done this are the ones who are getting increasing fines,” he said. “A lot of it is due to not understanding your customer book. If you don’t understand your customer book there’s no way you can really assess your risk. So there’s no way you can design an effective risk-based approach.”
Meanwhile, regulators like the Financial Crimes Enforcement Network (FinCEN) and The Wolfsberg Group are taking the initiative, too, moving away from today’s existing and somewhat inadequate anti-money laundering rules. Many of today’s frameworks reward financial institutions that meet seemingly arbitrary benchmarks and tick specific boxes on checklists based on rigid computer logic. In the real world, such checks remain largely ineffective.
“The good news is there’s a collective action problem around this, implementing standards like the Global Legal Entity Identifier, but that process is only a few years old,” Mesko said. “They cover around 50 million securities and companies, but we estimate there’s probably half a billion companies in the world.”
Further reading: Deep Dive: How AI- and ML-Powered Analysis Can Improve Fraud Detection and Investigation
Mesko wants to see a change in the way regulators examine financial institutions so they’re not just ticking different boxes on a checklist.
“They need to come in and ask questions like what is the institution doing to measure and assess risk and what are they looking at internally,” he said. “They need to look at things such as the effectiveness of their engagement with law enforcement, how useful the information they provide to them is. Theoretically that’s the way this should work, and it would be a huge departure from the way things were in the past.”
Such sweeping regulatory change, combined with more actionable intelligence of the kind Sayari delivers, 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 industry, with institutions avoiding certain regions and lines of business because the risk of fraud is deemed too high.
But Mesko said there’s a lot of untapped opportunity in those parts of the world. Institutions just need a way to identify the legitimate businesses.
“In many of these places that you think are so high risk and low transparency, there is actually quite a wealth of information out there,” he said. “It may not be nice and neatly structured and easily available, but it is there and it’s in the public domain.”
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