AI has improved and developed at an unprecedented rate over the past decade. This has been necessary as fraudsters across the globe have also been keeping up with technology, enabling them to create new scams and commit crimes like money laundering: exploiting old means of protection like outdated AML systems. Evidence for this can be seen in the fact that banks in the US faced $10.4billion in fines due to money laundering violations in 2020.
Lucinity is an AML software company, founded in 2018, with offices in New York and Reykjavik. Using advanced AI systems, the company helps banks discover money laundering to stop the funding of serious crime across the world. Guðmundur Kristjánsson, CEO and founder, more commonly known as GK, is an experienced veteran in the compliance space. Before founding Lucinity, GK served as Director of Compliance Surveillance Technology at Citigroup. He was instrumental in charting Citi’s path to AI in surveillance and responsible for a number of successful products across the compliance space. Before joining Citi, GK served as Director of Product Management at Nice Systems, building and delivering compliance systems to top-tier banks all over the world.
At Lucinity, Justin Bercich leads the effort to bridge the Human-AI dichotomy as Head of AI, by building trust and synergies between investigators and machines to improve financial crime detection. Bercich manages the implementation of production-ready distributed machine learning systems that use cutting-edge explainable AI algorithms and graph technology. Before joining Lucinity, Bercich worked as an Artificial Intelligence specialist at the Financial Conduct Authority, the financial regulatory body in the United Kingdom, for several years. Bercich holds a PhD in Machine Learning and Artificial Intelligence and Bachelor of Commerce (Honors) from the University of Sydney.
So what can be done to improve the AML system? Kristjánsson and Bercich have voiced what they believe the government and private sectors must do:
When it comes to money laundering, it can be easy to feel defeated. Banks worldwide are dealing with the ramifications of money laundering violations, and last year alone, faced $10.4billion in fines. In the US, Capital One had to stump up nearly $400million for failing to report suspicious transactions. These fines sting, but are more worrying evidence of a disjointed anti-money laundering (AML) system – one marked by cracks that sophisticated criminals can exploit and a slow uptake of new technologies that could root out and stop money laundering at the source.
Are we losing the financial crime fight?
Headlines decry a loss in the war on money laundering, but the war is in fact on crime – a battle that has been waged for thousands of years and fuelled by dark money. But we have made incremental progress, such as limiting the power of the American Mafia, or taking on the financing of terrorism. Still, criminals will always seek to stay one step ahead of the game to avoid capture.
Regulations and reporting frameworks tend to incentivise institutions to be diligent at reporting financial crime, covering all their bases along the way. Processes based on rules unwittingly breed box-checking behaviours, resulting in unmanageable volumes of Suspicious Activity Reports (SARs), versus a preventative interrogation of information in its risk-based context. These rules are based on computer logic and merely make our systems better at determining who a customer is instead of analysing what a customer is doing, which is a vital part of a risk-based AML process.
Further, data and compliance often sit in separate houses. Distributed and disconnected legacy systems lack a nuanced blend of quick computation augmented with a human ability to connect dots. And when the systems on which they rely are acting in isolation – and don’t account for complexity – institutions may be able to root out smaller money launderers, but the big players at the centre of large networks evade discovery.
Or is it game on?
When the only way to beat an invisible enemy is to be invisible ourselves, we can leverage the power of Artificial Intelligence (AI) and machine learning to augment our financial crime-fighters to understand variability and uncertainties that have skewed the equation for far too long. Changes in technology allow us to make AI explainable and therefore actionable. Still, looking across the US, we see that regulators and banks are making well-intentioned efforts, but need to embrace technological innovation – and that’s largely because the burden falls on them first to understand the systems, then to explain them to the “powers that be” to approve and implement them. The pace of change can understandably be slow.
That’s why we call for a unified approach that not only puts tech at the epicentre of crime fighting teams, but that also helps us bridge the gap between financial institutions and regulators in the US and across the globe. This will lead to a connected and big-picture view of the ecosystem and patterns that expose financial crime. Working together, tech can help institutions shift their focus from implementation to outcomes and de-risk the proposition of tech-led solutions among the risk-averse.
Money laundering is too big for anyone, or any one tool, to solve it in isolation. An aligned approach will enable banks on the frontlines to be our protectors, allowing them to use the tools that are best suited for their business as they explore new technology, like AI, to help them be the best citizens they can be.
It’s time for a shift
The seemingly ever-increasing fines assessed against financial institutions for AML and sanctions violations are leading to a call for personal liability, one in which the threat of a prison sentence is more impactful than financial penalties that may leave large banks unfazed. This attitude, however, sets governments and regulators against the financial sector, hindering the cooperation needed to solve the money laundering problem.
The government and private sectors need to work more cooperatively to explore, confirm and deploy new technologies in shorter cycles (and at a price that smaller financial institutions can afford) so that we:
- Stay ahead of criminals outmanoeuvring the rules by adding “friction” to money-laundering
- Make money laundering less attractive, whether because criminals know they will get caught or because it becomes too expensive
- Enhance our ability to follow the money up-stream to the sources of illicit finance
- Build upon the Financial Crimes Enforcement Network (FinCEN’s) program to bring new technologies into the banks and the regulatory system on a continuing basis
US legislation passed at the end of 2020 gives the private and public entities focused on preventing money-laundering the impetus to create a collaborative approach to combating money laundering, with human-directed AI at the centre.
Making the shift to enhanced technologies and putting rules-based systems behind us will not be easy; it will require a different framing of the AML world, from “know your customer,” to “know what the customer is doing and for what purpose?” And, in view of the push for real-time settlements, this information must be available almost instantaneously. Such a shift requires regulators to change the questions they are asking and to invite next-generation technology into the AML system.
Issuing a call for cleaner money
Vulnerabilities in our financial systems are an issue of critical national infrastructure – but they don’t just compromise our security; they also compromise the way our banking systems can adapt to our needs and behaviours. After all, banks spend an inordinate amount of time on money laundering reporting. If we could redirect that headspace to innovation, consumers would benefit from future-forward systems that keep money flowing freely – with the added confidence of knowing their money is clean. This is increasingly important when an American public is demanding from businesses to do the right thing by the world and the people in it. When we call for unity with regulators and financial institutions, we must ensure this holistic approach is inclusive of and reflected in society.
With the right technology and a united effort that recognises the scale of the money laundering problem, appreciates innovation and strives for a better financial system, we can finally shine a light on dark finance. We can move from a state of transmission – when individual parts of the system do a lot of talking – to a state of transformation – when we all walk together towards a new future and lasting societal change bolstered by a safer, more secure financial system.
