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EXCLUSIVE: CEO Of Lucinity Discusses Human AI, Outdated AML Practices, And Crypto’s Influence On Money Laundering

Last week, we got a chance to speak with the founder and CEO of Lucinity, Gudmundur Kristjansson, usually going by GK. Being recently awarded the “Newcomer of the Year” title at the Nordic Startup Award, Lucinity is the new sensation in the AML startup sphere. Founded in 2018, Lucinity uses a Human AI approach to combine the best of both human and artificial intelligence and help banks deal with the threat of money laundering. Despite being a relatively young startup, the Icelandic startup now has offices not only in Reykjavik but New York, Brussels, and London as well, which shows how successful they managed to become in the past two years. As GK explains, although the startup has only been on the market for a little over two years, the experience of the team members goes a few decades back and stretches over the AML industry, compliance industry, and the AI sector. 

Click here to watch the full interview with GK 

How Does Lucinity Differentiate Itself In The AI And AML Startup Scene?

“What Lucinity does is best captured in our motto: ‘help banks make money good,’” he says. “We love doing that through our SaaS-based solution that helps the organization not only become more productive in their AML measures but gives them a better understanding of what’s going on in their transactional data, what is going on with the clients, and really helping them know their customers and what they are transacting on a day-to-day basis”

GK has been in the compliance technology industry for more than 10 years, starting at NICE and bringing several products to market there, followed by over three years at CitiGroup. 

Being a veteran in the industry, GK understands just how serious of an issue money laundering is. According to the United Nations Office on Drugs and Crime, each year up to 5% of the world’s GDP is laundered globally, which is approximately $2 trillion. 

Considering the challenge that money laundering constitutes, it is not surprising that the anti-money laundering solutions market is a dynamically growing field, with the expected compound annual growth rate (CAGR) of 16% in the next five years and a big number of startups focusing on the AML solutions appearing on the market. 

“What really differentiates us is how we implement artificial intelligence in production, and not just on a laptop in a basement somewhere,” GK says. “The second thing that differentiates us is that we create products, not projects. Our SaaS application is ready to be deployed when you, the customer, actually need it. It’s not a one-and-a-half-year exercise of pros and cons, it’s actually ready to be used. In fact, you can start to enjoy Lucinity on day one when you sign up.”

Moreover, Lucinity allows their client to test the applications before they make a purchase so they can go through the interface and get a sense of how the software works. 

“Instead of emphasizing only the analytical capabilities we have, we emphasize actually being able to explain what we find in our AI algorithms,” GK adds. 

Lucinity Takes AML One Step Further, Focusing On The Customer Rather Than The Transaction

According to the global report on anti-money laundering solutions, one of the largest trends in the AML sector is transaction monitoring. Identifying and analyzing suspicious transactions through KYT (Know Your Transactions) is utilized by a large portion of the AML startups. Lucinity takes it one step further and instead of the transaction, they focus on the customer through their newest feature, the actor-based review.

“We believe that KYT is broken because it relies on self-reporting data points,” GK says. “Rather than just relying on self-reporting data and just having a process in transaction laundering that is not connected with the KYT, actor-intelligence connects the two together and assesses the customer based on both the self-reporting data as well as the transactional behaviors and creates an anti-money laundering risk score. This score allows you to focus on the riskier flaw in your customer and assess them based on the behavior rather than just self-reporting data. This really brings up the question, ‘do you really know your customer?’, because with the solution you are starting to know them better and how they behave”

Leveraging The Strengths Of Human AI

With the emerging technologies assisting the acceleration of the majority of sectors, the AML companies are also leveraging the strengths of AI, ML, and Cloud Computing. Many experts believe that AI is the holy grail that will transform the traditional and time-consuming approach to money laundering into a much more effective process.

Traditionally, financial institutions have been using a rule-based approach to AML. They were collecting information about customers of business services but these customers were constituting only a small percentage of their customers. Thus, the banks did not know the majority of their customers and due to the lack of risk management awareness, tools, and expertise, even if the banks managed to collect information on customers and their transactions, they were not able to effectively apply it to the AML regulations.

The strengths of AI allow financial institutions to shift to a “risk-based” approach where the accurate evaluation of a risk level is the foundation. It transforms the AML practices from post-analysis and judgment to proactive management. AI makes conducting due diligence on business operations, industries, customer characteristics, and regions easier, allowing the banks to obtain the complete customer information. 

While Lucinity is also using AI, they focus on human AI. The company uses the so-called shared intelligence that brings together the humans and the machine and enhances the human skills with the power of the computers. This leads to a lot of institutions being penalized by regulators for AML violations and a system that prevents the banks from accurately analyzing the risks of money laundering. 

“There are a wide variety of algorithms that can apply in different circumstances, but what is common with all artificial intelligence methods today is that you need some kind of human intervention,” GK says. Human AI is adding this contextual talk to the users, so they are able to understand why the algorithms are doing what they are doing, and allowing the humans to visualize what is going on and apply the toolbox that humans are good at. So the humans are good at the insightfulness, the thinking inside the context of just the data and when you give them that tool of applying human intelligence and AI together you start to see new things and you start to actually speed things up”

“At the same time, you also do it from a compliance perspective in a much more orderly fashion, because you are creating bigger trust between humans and the AI,” he adds.

Although the need to transform to a more risk-based approach to AML has already been highlighted in the early 2000s, the process is taking a long time and a lot of the financial institutions are still using outdated systems to prevent money laundering, risking not only an increase in illicit activities but also hefty fines. As GK explains, although AI has been deployed for several years now, the human AI, explainable AI, and contextualized AI that Lucinity is leveraging has “only been achievable in the last two years”. 

Moreover, leveraging AI does not only come down to having the right technology available but also getting the regulators on board. GK believes that for a long time, the technology sector struggled with explaining how AI worked and proving its efficacy. 

“Our head of AI comes from a regulator background and the problems are not with the regulators saying you can’t use AI, but it is often the translation made with the compliance people and the AML people that they are not comfortable enough with the new technology to explain it to the regulators and there the tech industry completely failed in the way they explain technology to compliance individuals who need to explain it to the regulators,” he says. 

“Therefore, it is so important that we explain these new tools in a cohesive and easy to comprehend way, and that is why human AI will help us move this industry forward, away from the errors coming from technology that was invented before the iPhone to actually modern technology, and will help the banks make money good,” GK adds. 

Covid-19 Causing A Large Spike In Financial Crime And Traditional AML Unable To Keep Up With The Pressure

The need for the use of emerging technologies in the AML industry became even more pressing as the Covid-19 pandemic started. Historically, we have seen large crises causing a large spike in financial crime and the same situation happened last year. The Financial Action Task Force (FATM) has highlighted that Covid-19 did not only bring the surge of financial fraud and money laundering, it has also made the regulators’ job a lot harder. The FATF stated that “the pandemic has severely impacted some authorities’ ability to implement measures to detect, prevent and investigate money laundering and terrorist financing.” Apart from the pandemic, financial institutions have to manage the challenge of digital currencies becoming more and more prevalent. If the traditional AML practices are already struggling with effectively managing risks, will they be able to handle additional pressure coming from digital assets in terms of potential criminal activities?

“Absolutely not,” GK says. “For starters, the pace is much faster. The systems as a whole don’t even understand digital wallets, they try to interpret everything as “us, us, us,” as it was in the 90s up to 2003, and so you need new kinds of software that actually understand this and you need new kinds of research. I believe that crypto is a fantastic thing that will help us move forward. We just need this maturity step and adoption by the technology space, by the compliance space, as well as by the cryptos into realizing why we all need help to make money good.”

The Need For Regulations In The AI Market

One of the biggest topics surrounding AI right now is the need for regulations. While some experts argue that at this point in time, introducing strict regulations will only slow down the growth of a sector that is in its infancy, others claim that creating an adequate regulatory framework is a pressing issue that must be addressed as soon as possible. 

In the past few weeks, we were reporting on the efforts of regulating AI and the EU proposing the draft regulations that have resulted in a lot of criticism both from the skeptics and the enthusiasts of AI. One side has been arguing that the proposed regulation leaves too many loopholes and is too vague to effectively function. The other side has said that the regulations are too strict and are disrupting the growth of artificial intelligence and the opportunities it brings in several sectors. 

“From a regulation perspective, I think they are vital to guide us, but they should be vague because the speed at which we need to improve is greater than we can think of all the scenarios,” GK says. “Will there be mistakes? Yes, absolutely. But we need to create a collection of people that use AI for ethical reasons and we create checks and balances and not just write everything into law or regulation that we can’t even imagine where the applicability is right now. 

“For us, we just take extreme privacy seriously, we take extreme experience seriously, how and when algorithms are applied and then it’s about transparency. So I think that if I were to recommend one thing, it would be to really push for this transparency and openness, and what is being done and how it is being done. That is already enforced in the AML regulations around the world,” he concludes.

Take a look at our previous in-depth interviews with CEO’s of most up-and-coming tech startups:

Joe Robinson, CEO Of Hummingbird, Discusses FinCEN Leaks, FinTech, And AML

EXCLUSIVE: How Can Every Company Be A FinTech? Interview With Tarun Bhasin, The CEO of Kunai

Nel Tomczyk
Nel Tomczyk
Nel is a writer for RegTech Global. From her past experience, she gained a deep understanding of fintech, AI, regulations, and new technologies. She has vast experience in journalism and entrepreneurship.


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