By Kleo Papas, Director of International Projects (GovRisk, UK)
Global Money Laundering (ML) accounts for up to 5% of global (gross domestic product (GDP) – or $2 trillion – every year, according to the United Nations Office on Drugs and Crime (UNODC). Banks and law enforcement agencies are having to explore new methods to combat this huge and ever-growing problem and are increasingly turning to Artificial Intelligence (AI) for help.
Money laundering, a term coined in the 1920s during the era of Al Capone, refers to the practice of hiding criminal proceeds or “dirty money” by introducing it into the legitimate banking system, through various means, such as buying art works, jewellery, cars, boats and real estate; in fact, almost anything that can then be sold on.
Once this happens, it then becomes almost impossible to track and trace the money, for example, many tech-savvy criminals have learnt that by quickly converting their laundered proceeds into Bitcoin or other such cryptocurrencies (and as of 2019, there are well over 1,600 such currencies in use) they are more or less in the clear.
So how effective are our financial regulators, supervisory authorities and law enforcement at stemming the flow of illicit money? Well, even the most optimistic estimates indicate that no more than 1% of criminal funds are currently being successfully confiscated – and they say crime doesn’t pay.
In the UK alone, Suspicious Activity Reports filed by the banks (SARs) increased by 10% in 2018, according to the National Crime Agency (NCA).
In the USA, the Federal Bureau of Investigation (FBI) is developing various “applied technical enhancements” for use as crime-fighting tools in the hope that they can keep pace with the lightning advances in financial tech (FinTech) – and they are not alone. Others that are also tasked with tackling illicit money-flows are similarly ramping up their use of AI to combat money-laundering.
In the vanguard of technical advancements is specially designed AI that “learns” to sift through vast volumes of transactions at enormous speeds. AI is able to spot patterns of suspicious activities in moments, whereas humans tasked with this would need months or even years.
AI has even proven better at spotting another key element of many a money laundering operation – the corrupt insider. The deployment of AI means that there is no danger of corrupt insiders purposely overlooking suspicious patterns or activities.
In recent years, some of the world’s leading banks and financial institutions have been embroiled in money laundering, and subsequent investigations into many of these cases have revealed a litany of violations, such as silencing whistle-blowers, wilful blindness, falsification of reports, failure to flag suspicious activities and tipping-off.
And although at first sight, some of the fines slapped on these miscreants appear to be colossal, and objectively they are, they clearly represent a drop in the ocean when compared to the estimated $2 trillion of global GDP attributable to illicit financial flows.
Consider UBS, one of the best-known Swiss Banks, that recently received a staggering fine of over $4 billion, after admitting to aiding some of their wealthiest clients hide billions of Euros from tax authorities and launder the proceeds. To put this into context, the recent GDP figure for the nation of Sint Maarten (only the Dutch side) is approximately $800 million or, if you prefer, 20% of the UBS fine.
And ING, a leading Dutch bank with a global presence, was meted out a comparative slap on the wrist with a fine of approximately $800 million for failing to report, let alone stop, organised criminals from laundering money through a complex web of accounts.
The head of Danske Bank, a leading Danish bank, resigned in the wake of an eye-watering $240bn money laundering scandal; and in Latvia, ABLV Bank, was ultimately forced to shut down after accusations that they were involved in large-scale money laundering operations and enabling some of their clients to violate nuclear weapon sanctions against North Korea.
New technologies such as AI are being increasingly embraced by regulators around the world, as the realisation is sinking in that otherwise they will continue to fight a losing battle. There is a major cautionary note; however, that AI is only as good as the data it is fed. Financial regulators, supervisory bodies, enforcement agencies and financial institutions have now recognised the benefit of sharing more information.
Small jurisdictions are much more at risk from money laundering than places such as London, New York, Paris and Zurich. It is therefore important that a holistic approach is taken and that legislators, regulators, banks and indeed all businesses with significant cash-flows work closely to develop a coordinated strategy for tackling financial crime and money laundering.
Kleo Papas is a founding member and has been Director of International Projects at GovRisk since the company formed in 2010. During this time, he has worked on a multitude of projects across the globe focusing on AML/CFT, Anti-Corruption and Bribery, Transparency & Integrity, Procurement, Cybersecurity, Compliance, Good Governance and Financial Crime Prevention related work.
He has worked primarily, though not exclusively, on donor-funded projects for the UK FCO, USAID, EBRD, World Bank and EU. His experience includes the design, delivery and management of complex multi-stakeholder projects in the Caribbean (Trinidad & Tobago, Antigua & Barbuda, Cayman Islands, Aruba, Curaçao and the Bahamas), Asia (Armenia, Jordan, Hong Kong, Malaysia, Indonesia, Vietnam, Papua New Guinea, Philippines and Thailand), the Americas (USA, Belize, Guatemala, Nicaragua, Panama, Colombia, Brazil, Peru and Chile) and Europe (UK, Latvia, Lithuania and Turkey).
New technologies for Anti-Money Laundering and Combatting Financial Crime will be featured at the upcoming 4th Dutch Caribbean AML & Regulation Forum, June 4-7, at the Sonesta Maho Beach Resort in St. Maarten. To register, go to www.govrisk/DCGRF2019 or email This email address is being protected from spambots. You need JavaScript enabled to view it.
