The rapid improvements in the payments industry over the past decade have had the unfortunate side effect of making money laundering more of a challenge for institutions to detect and deter. With a greater number of methods for exchanging money and with most transactions happening digitally, it has become harder to chase after money launderers’ latest tactics. According to the UN’s Office on Drugs and Crime, more than $1 trillion is now laundered worldwide.
During a recent PaymentsJournal podcast, Amber Goodrich, Compliance Analyst at CSI, a leader in the fintech, regtech, and cybersecurity solutions space, sat down with Kevin Libby, Fraud & Security Analyst at Javelin Strategy & Research, to discuss how money laundering has changed in recent years and what companies should be doing to deter it.
An Ever-Changing Backdrop
The world of exchanging assets has changed dramatically since the initial rules and regulations aimed at money laundering were put into place years ago. “You don’t know who you’re doing business with,” Goodrich said. “And we’re seeing many different types of currency coming into play.”
For one thing, the Anti-Money Laundering Act of 2020 has yet to be finalized, with new regulations still being proposed. New rules are being rolled out to increase penalties, and discussions are centering on imposing multipliers on individuals found to have committed repeat offenses. The subsequent uncertainty has made it harder for institutions to find their footing.
“The thing that we’ve seen the most guidance on is the beneficial ownership piece that’s set to go into effect early next year,” Goodrich said. “But even with that, there’s still a lot that hasn’t been defined yet.”
Criminals are using social media to contact and enlist recruits, making it harder to detect laundering efforts. “Criminals are using money mules who have never been involved in the practice, so there’s no prior data to use to identify them as potential money laundering parties,” Libby said. “All of those things make it harder for financial institutions to meet those regulations at all, let alone not have repeat problems if they’re getting behind on alerts or having trouble making those connections.”
One of the most frightening developments is that professional groups are being established specifically to launder money, which presents a distinct problem for financial institutions. It can be very difficult to identify connections between parties that might have an association with a money launderer. And these cabals have been hiring professional accountants and lawyers into these organizations with the purpose of more effectively laundering the money and with greater levels of secrecy.
The Challenges of Keeping Up
Goodrich said she has increasingly heard from financial institutions CSI works with about how hard it is for them to keep up with the amount of reporting they are required to do. Budgets are a limiting factor in combating money laundering, but regulators don’t consider budgetary constraints legitimate reasons for not complying with requirements.
“Modernization is a term that they use, but it’s not defined on what they want us to do with that,” Goodrich said. “They don’t necessarily come out and say you need to go out and invest in new software systems, or you need to completely overhaul your policies and procedures to make sure you’re up to date on these things. But it’s implied.”
Even absent these provisions, most institutions would be happy to rely on the latest state-of-the-art technology, using machine learning and artificial intelligence. This would allow organizations to adapt their rules on the fly to recognize emerging trends in money laundering more effectively and to make connections between pass-through accounts.
The Role of Artificial Intelligence
CSI has an anti-money-laundering (AML) solution that offers artificial intelligence and machine learning as a part of it. “That’s huge because old systems for AML and transaction monitoring are not enough anymore,” Goodrich said. “You need systems that have smarter types of alerts that can look at past behaviors that your customers have and see where the changes are happening, without having to manually review reports and create spreadsheets.”
According to Libby, a positive of the recent regulatory moves could be that they prompt institutions to get over the jump and invest in the technology they need.
“As Amber suggested, it’s saying with a wink and a nudge that you need to invest in these new technologies,” Libby said. “That could go a long way toward streamlining processes.”
AI automated systems could reduce the burden that excessive reporting creates for institutions. Integrating AI involves some pain points but also some opportunities. Financial institutions should focus on the latter. Compliance is required, painful or not.
As far as compliance risk, CSI has been seeing violations involving multiple regulatory agencies have been involved. A single compliance deficiency may be cited not just by the Financial Crimes Enforcement Network but also by the Office of Foreign Assets Control and even the Department of Treasury because it may be related to a sanctions program. There’s risk of criminal violations that come along with it as well for Bank Secrecy Act and AML officers: If they are cited for something, there can be criminal penalties for them individually.
Key Takeaways
One of the most important things for organizations to do is combine all of their data and get a holistic picture. A solution that offers API technology can bring that together and provide a whole picture of who an institution’s customers are engaging with in business.
Data integration is a huge part of being able to effectively identify money laundering activities that follow current trends and those that might emerge in the future. Data is everything in that regard, and the more seamless an integration is across an organization, the better.
“How do you decide who was a high-risk customer or a low-risk customer, especially when you’re working with limited data?” Goodrich said. “We offer risk scoring that can help you decide how risky your customers are.”
Artificial intelligence and machine learning will be critical components as anti-money-laundering technology evolves. The sheer number of parameters that can be tested—and the interaction between those parameters—can only be teased out by a computer system.
It’s never too soon to start establishing an anti-money-laundering protocol. FIs shouldn’t wait until the regulations settle on a hard start date, leaving organizations behind the curve. One area that hasn’t yet seen much regulation is cryptocurrencies, a huge risk to financial institutions even if they do not realize they are doing business in that area. Don’t wait for regulations to get started on a crypto AML plan.
