Deep Dive: AI Takes On Global Money Laundering
AML

Deep Dive: An Automated Approach To The $2T Global Money Laundering Problem

The term “money laundering” is believed to have originated as a result of infamous Chicago gangster Al Capone’s habit of channeling criminally obtained funds through laundromats, as their cash-heavy nature made it more difficult for the right side of the law to detect such money mixed in among legitimate payments. Money laundering has evolved greatly since Capone’s activities in the early 1900s, though, and it is becoming harder to combat. As such, the regulators and FIs seeking to crack down on these activities have their work cut out for them. 

Approximately 2 percent to 5 percent of the world’s gross domestic product (GDP) – $800 billion to $2 trillion – is laundered each year, according to a report from the United Nations Office on Drugs and Crime (UNODC). Digitalization, fast-moving payments, international trade, global economic interconnections and a lack of standard regulations across markets all make money laundering and CTF immense challenges for FIs – something firms are working hard to address. 

Rethinking Security Strategies 

Money is the lifeblood that fuels any organization, and that includes terrorist and other criminal groups. FIs that fail to catch illicit activities miss opportunities to hamstring such operations and unwittingly help fund them. They can also find themselves facing significant fines if they fail to sufficiently adhere to rules designed to catch money laundering. Worldwide, banks paid out approximately $321 billion between 2009 and 2016, for example, because they failed to comply with money laundering, terrorist financing and other regulations. 

Financial and Ethical Security Strategies

Faced with these dual motivations, banks around the world are investing in regulatory compliance, and were projected to spend more than $8 billion on AML compliance by 2017. They need to be certain their compliance dollars are making a tangible difference in their AML and CTF successes, however, and must keep up as regulations change and governments level economic sanctions against other countries. In addition, FIs must keep pace with fast-moving international money flows as they monitor transactions.

Many have already begun hiring more investigators and other relevant staff in response, with some major U.S. banks increasing their AML compliance staffing tenfold between 2012 and 2017, according to one report. This approach can be costly, however, and doesn’t always have enough impact if new employees aren’t simultaneously supported by efficient procedures and technology. In too many cases, FIs create separate compliance programs based on specific countries, products and customer bases, which can be inefficient and expensive, and FIs’ efforts may suffer from manual, siloed and inconsistent processes. 

Turning to Automation Tech

Against this backdrop, attention has turned to the support offered by automation tools like artificial intelligence (AI), which can free up staff to focus on higher-risk cases. These solutions are tapped to reduce human error, perform data analysis, identify connections, provide a more holistic view of potential money laundering and speed up decision-making processes. AI could be particularly meaningful in analyzing customer behavior for suspicious activities, accelerating customer validation, automating suspicious activity report filing and mapping interconnections between clients, among other areas. 

Expectations have been high so far. One report estimates that AI applied to KYC and AML, authentication, compliance and data processing in the global banking industry could reduce costs by 47 percent. Other researchers claim that, in some cases, allowing machine learning algorithms to analyze transactions has led to a 20 percent to 30 percent reduction in false money laundering reports. 

With money laundering being a global financial problem, FIs need to equip themselves with as many tools as they can. As they’re facing hefty fines for insufficient adherence and wrestling challenges related to evolving financial and regulatory ecosystems, they need to be sure their AML and compliance budgets are invested where they’ll have the greatest impact. Reports suggest automation and AI could become increasingly important for compliance-conscious FIs. 

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Latest Insights: 

Our data and analytics team has developed a number of creative methodologies and frameworks that measure and benchmark the innovation that’s reshaping the payments and commerce ecosystem. The May 2019 AML/KYC Tracker, provides an in-depth examination of current efforts to stop money laundering, fight fraud and improve customer identity authentication in the financial services space.

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