Black Logo

Our site uses cookies to give you the best user experience and to collect and share information for analytics, advertising and personalization on this and other sites. Please select whether you consent to our use of cookies and related technologies (“Cookies”), as described in our Cookie Policy. You can return at any time from the same web browser to update your preferences. Please note that resetting your browser’s cookies will reset your preferences. You can control the use of some types of cookies through the Cookie Settings below, but note that if you choose to disable certain cookies, it may limit your use of certain features or functions on our services and websites.

Privacy Policy
Strictly Necessary Cookies

These cookies are required to enable core site functionality.

Functionality Cookies

Functionality cookies allow us to provide enhanced and more personalized content and features. In order to permit your connection our website, our servers receive and record information about your computer and browser, potentially including your IP address, browser type, and other software or hardware information. All of these features help us to improve your visit and assist in navigation of the sites’ features.

Analytics Cookies

We and our service providers may use analytics cookies, which are sometimes called performance cookies, to collect information about your use of our website, for instance, which pages you go to most. The information allows us to see the overall patterns of usage, help us record any difficulties users may have while using our website and can show us whether or not our advertising is effective.

Advertising And Targeting Cookies

We may use third party advertising and targeting cookies to correlate your use of our website to personal information obtained about you so that we may more clearly target the information we provide you to the specific items we think you will find interesting, based on your prior online activities and preferences. We also may use these cookies to deliver ads that we believe are relevant to you and your interests.

For more information, view our Cookie Policy


Machines vs the mob: Fighting money laundering & terrorist financing with machine learning

Legility , Sarah Brown 06 / 19 / 19

Consider for a moment the problems of criminals: The money they earn, spend, transfer or move – as a result of crimes committed or as part of the planning process for crimes as-yet-to-be-committed – has the high potential to raise the suspicion of law enforcement. If criminals wish to live to fight another day, they must conceal the origins of their ill-gotten gains – typically by “laundering” their money.

"Our department is dealing with the COVID crisis on top of several attorneys already being out of the office for extended periods,” says the Director of Legal Operations for a large organization. “Having access to flexible legal talent through Legility has allowed us to quickly tap resources to augment our attorneys and provide seamless customer service."

Despite all this, money laundering is a $3 trillion per year industry, making up 5% of the global GDP.


Money laundering and terrorist financing

Concealing the origins of their illegally obtained or about-to-be-used illegally money can be quite the undertaking: Criminals and would-be terrorists often pass money through a complex sequence of banking transfers or commercial transactions, with the ultimate aim of returning it to themselves in an obscure or indirect way – now “clean,” having been erased of all traces of criminal activity on its trip through the financial system.

For that reason, the financial services industry has long been tasked with implementing anti-money laundering (AML) detection systems. Organizations have a real incentive to detect and quash money-laundering activities: As fines from regulatory organizations increase, so too has the call for holding compliance officers, senior executives, and board members personally liable for failure to have an adequate AML program and transaction monitoring system (TMS) in place.

Since the 2008 financial crisis, fines levied against financial institutions have been huge and are continuing to increase – we’ve seen $26 billion in fines levied since 2008 alone. Despite all this, money laundering is a $3 trillion per year industry, making up 5% of the global GDP!! So while we’re spending a lot in fines, in enforcement, we’re still not getting to those transactions that are illegal.

Automating anti-money laundering using machine learning can reduce the plague of false positives.

Anti-money laundering transaction monitoring systems

Transaction monitoring systems are an incredible boon to the anti-money laundering and compliance profession: The sheer number of financial transactions that occur daily across the globe are too vast for even a legion of very focused humans to monitor. It would be near-impossible for even the sharpest of eyes to detect even a fraction of suspicious transactions without the help of automated detection systems.

However, the systems employed today come with a troubling downside: Transaction monitoring systems throw off a huge number of “false positive” results. Studies have shown that 90-95% of the triggers and alerts these systems generate are false positives and the financial institutions are spending a huge amount of money on humans to then go into these transactions and investigate them.

What does that mean? Any alert generated by a TMS must be investigated, assessed, or otherwise reviewed by a human being who can employ context, skill, and subject matter expertise to either resolve a transaction or escalate it.

And human beings are quite expensive. The more false positives automated TMS creates, the more expensive the compliance process becomes.

The plague of false positives in money laundering investigations

There are a number of reasons for this “plague of false positives.” One way is related to how we try to detect suspicious activity:

The traditional approaches are rules-based, employing if/then statements. As a simple example, one system may flag every transaction sitting just below a reporting level – let’s say $10K. So any transaction at or above $10K may need to be reported to financial regulators, so money launderers would try to structure cash that’s going into the financial system just below $10K so it doesn’t trigger that reporting.

However, money launderers aren’t careless in this way anymore – they’re savvier in the way they introduce cash into the system. Regardless, hundreds of transactions at, say $9K or $9.5K will trigger AML systems that require time-consuming, expensive human review – while real money laundering transactions may go undetected.

Another problem is the massive amounts of data involved in the global financial system.

Huge amounts of data sit within financial institutions – scattered throughout disparate data systems – some sitting in customer documents, in archived financial systems, in emails, even some data can be found in news reports and on social media. Many governments have also built databases to identify ultimate beneficial owners, so data found within those systems must be scoured and matched up with financial transaction data. There’s so much data that a human can’t make sense of it all – let alone see the links needed to identify the criminals hiding within the sea of financial data.

Unsupervised machine learning can specifically target transactions that mirror the way money laundering and terrorist financing is currently happening.

Automating anti-money laundering

Enter two types of machine learning: Unsupervised machine learning, and traditional supervised machine learning. Together, coupled with human expertise and some of the older approaches, these techniques have the potential to end the plague of false positives to help the experts uncover – and stop – the real money laundering and terrorist financing transactions.

The difference between unsupervised machine learning (UML) and traditional machine learning is that in the case of UML, it doesn’t require massive amounts of human training before the system becomes “stabilized,” nor does it require the highly organized, labeled data that you need in traditional machine learning.

Unsupervised machine learning

UML can detect links that traditional, trained machine learning and humans just aren’t able to detect. Oftentimes, in money laundering, the source of the wealth – and the ultimate beneficial owner – are hundreds of layers deep – so you are looking to connect very small pieces of information like IP addresses and locations and so on – and UML is able to detect relationship patterns and links very quickly.

Particularly in the wake of Panama Papers and Paradise Papers, the need to understand ultimate beneficial owners has greatly increased – regulators are certainly going after this more. Government databases in particular are difficult to read, but UML can read and make sense of the data.

UML can also detect patterns which go beyond if/then – so it’s looking at patterns that have been built by known money laundering transactions and these patterns are much more intricate than the rules approach would tell us.

Reducing false positives, increasing safety

Many financial institutions must process the high volumes of false positives – choosing not to do so would leave them vulnerable to astronomical fines and worse – potentially letting funding for the next terrorist attack slip through the cracks.

However, through skillful application of a combination of technologies and techniques – unsupervised machine learning, supervised machine learning, traditional rules-based approaches, and expert review – the high volume of false positives can be lowered, thus freeing up time and resources to investigate potentially dangerous transactions.

The latest UML approaches have been designed to specifically target transactions that mirror the way money laundering and terrorist financing is currently happening. Using these newer approaches allows financial organizations to more easily and efficiently meet their regulatory and moral obligations – successfully applying these models to quickly uncover the transactions that need to be investigated, and put a stop to them immediately.

Use technology to fight terrorist financing.

About the author


Legility, a leading provider of technology-enabled legal services, provides consulting, technology, managed solutions, and flexible legal talent to corporations and law firms. The company has more than 1,000 lawyers, engineers, consultants, technology and data specialists, and operational experts serving more than one-third of the Fortune 100 and one-quarter of the Am Law 200. Legility helps its clients improve operational efficiency. By combining people, processes, and technology, Legility offers innovative and bundled solutions that align with how the legal market is increasingly looking to engage.

Subscribe to Insights

Your one-stop shop for the Legility logo & more.

Your one-stop shop for the Legility logo, brand guidelines, photography assets, and more.

Subscribe to insights