3 min read

Go Under the Hood of the QuantaVerse AML Software Platform with Founder David McLaughlin


Watch this video to learn more about the QuantaVerse Platform

David McLaughlin’s journey in creating the QuantaVerse platform and the problems he set out to solve makes for a fascinating story. It is one in which powerful technology supports human intuition for superior outcomes that can only be achieved by marrying the strengths of both.

Anti-money laundering (AML) compliance costs businesses huge amounts of money, time, and human effort. However, despite this investment and best efforts, McLaughlin understood that few get close to addressing the problem with the tools and resources they have. Improving AML efforts is a universal concern he knew could be addressed with advanced technology. 

Financial institutions face multiple challenges throughout the process of selecting, implementing, and maintaining their AML technology, teams, and processes. One of the key reasons that so much crime continues to go unabated is because many institutions rely solely on legacy technologies. As David McLaughlin points out, relying on transaction monitoring systems (TMS) can become a liability due to the high percentage of financial crimes it misses. The ramifications for institutions are high; $4.67 billion in fines related to financial crimes such as money laundering, bribery, and sanctions were paid in 2020. That is almost $1 billion more than the year before. 

Yet, the bigger cost is societal. The United Nations estimated that around $800 billion to $2 trillion are laundered every year (or 3-5% of worldwide GDP). Unfortunately, about 90% of this amount remains undetected. “The amount of dirty money flowing around the globe was something that made us develop AML solutions using artificial intelligence (AI). If we could meaningfully reduce the financial crime being missed, it would be something to be proud of,” McLaughlin said. 

How the QuantaVerse Platform Works 

The QuantaVerse platform uses AI and machine learning to fight financial crime. The platform is trained to analyze the transactional entity data across three primary components. Firstly, it examines the reputation of the people or organizations that are moving and receiving money. For example, it aims to find proof indicating whether their past actions might have illegal records. Secondly, the platform examines transactions for anomalies and if there is something about them that is different from established patterns. Thirdly, it analyzes the intent behind the transactions and evaluates if it makes economic sense for these parties to do these transactions. 

For example, an analysis by the platform found money flowing between a computer technology firm and a casino. At face value, nothing was abnormal about it. All the transactions fell within TMS rules in terms of value, velocity, and line of business. But what the machine learning capability found that the rules overlooked was that the money was flowing backwards. Instead of the casino buying things from the computer technology firm, the computer company was sending money to the casino where it could be laundered. The QuantaVerse platform found that the casino was practicing the same underhanded behavior with other “vendors,” all with common ownership, which included someone previously accused of embezzlement. 

Examples like this are possible because the platform can analyze more data from more sources than human investigators have the time to find or the capacity to process. Designed and developed specifically for AML compliance, the QuantaVerse platform also automatically scores risk associated with a case and presents digestible findings to investigators quickly through a proprietary Financial Crime Investigation Report (FCIR). 

The QuantaVerse Platform Hits the Ground Running 

Many customers often wonder how difficult it will be to integrate AI and machine learning solutions into their AML programs. As they quickly discover, it is not hard at all. The QuantaVerse platform links with existing TMS and case management solutions and flexibly leverages existing risk policies and models. The platform is easily configured to rank transactions according to where a financial institution does business. The system can also be calibrated for specific products, or specific customers, for criteria that are unique to the institution. 

The platform is continually updated to respond to changes in laws and regulations.

Making Human Investigators Superhuman Investigators

In addition to the AI capabilities of the platform, David McLaughlin stresses how it supports the human part of the AML process. AI and machine learning have not yet reached a point where they can perfectly identify financial crimes, investigate them, and report them to the government. Human interaction combined with technology is critical.

To maximize the use of AI and machine learning in AML investigations, human investigators need to have confidence that the platform has successfully scoured, sorted, evaluated, and presented data to them. Human intuition and “gut feel” are critical when reading the reports and correctly scrutinizing them to make the right decisions about what is risky and what is not.

To make sure that they’re not at risk of reputational and regulatory damage, companies need a triad of compliance strategy, investigative expertise, and technology solutions. The integration of managed services and the QuantaVerse platform by AML RightSource provides companies what they need to effectively and efficiently fight money laundering and meaningfully improve BSA compliance. “There's more that the customer needs than just tech,” McLaughlin notes, adding that with this integration, he is confident about making greater impact in the sector. “Of course, we have the technology that improves your compliance department, but we also have the experts that know how to make the most of it. The blending of world-class managed services and technology is a unique proposition to the market.”