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With the unrelenting rise in money laundering sophistication and activity, penalties for AML and Know Your Customer (KYC) failings have increased accordingly to a staggering $8.1B* in fines in 2019. It’s no longer the U.S. handing out the bulk of the penalties; it’s become more globally distributed.
By contrast, the technological investment to avert this insidious crime stands at $23.4B**, a mere drop in the bucket compared to the amount lost globally to money laundering.
The bad actors remain ahead of the game largely because they’re unhampered by aging technology and they answer to no one’s regulations.
How Did We Get Here?
While capabilities vary from institution to institution, the main obstacle has been the manual nature of managing AML data and the associated false positives and low detection rates that accompany this problem. On the surface, detecting money laundering sounds simple: generate a suspicious activity report, investigate whether it’s truly suspicious and report it to the regulators. In practice, however, it’s not so easy to determine suspicion because information comes from multiple sources, and the more data we accumulate, the longer it takes to process it.
While the key lies in automation, the solution you choose must integrate with your existing technology, and that takes time. Further, even for institutions with the best internal communications, siloes exist. Risk and compliance teams are looking for streamlined data management that uses artificial intelligence to detect patterns and correlations, however when taking a cross-silo approach, issues can occur. Without the power of artificial intelligence and machine learning, you may never see the big picture within your own organization.
The Challenges of a Connected World
In our digital world, it’s become much easier for criminals to move money around the globe and escape detection. To fight back, firms are investing in advanced compliance technology that helps combat and control fraud – with early detection and continuous monitoring through the life cycle of a transaction.
The main challenges lie in detecting patterns and trends, connecting disparate sources and types of data, managing high volumes of increasingly sophisticated transaction types, all while working in an increasingly shortened detection time frame and remediation period. Even with the best in-house IT experts, you can’t go it alone. Be sure to seek technology partners that have the demonstrated ability to help you streamline your workflows and deal with emerging disruptions – both criminal and non-criminal.
Innovation is the key. The regulators expect it, and without it you fall behind and you’re not managing risk as well as you could. The trick is to find the right problem at any point in time. While there’s no right or wrong answer, your interim objective might be automating manual tasks or moving away from a batch processing mindset or whatever is decreasing your productivity while increasing your risk. Whatever problem you are tackling, cost constraints are always a reality, so you’ll have to find ways to make the proposed improvement cost-neutral or, even better, ROI positive.
System upgrades must move you to a real-time reality, and not just on the front end but all the way through to the back office. To get there, you’ll be exploring innovations that include AI, machine learning, robotics and more. No matter what your immediate goal is, or how you get there, many institutions have found that unexpected results come with any change, whether that’s reduced false positives of more efficient internal handoffs and/or improved customer relations.
The Promise of AI
A recent FIS poll indicated that in terms of AI maturity, the average survey participant rated their institutions as a 2.18 out of a possible 5. They further indicated that their biggest challenges in adopting AI for AML are their legacy technologies and the state of their data, followed by budgetary restrictions, the culture of their firms and regulatory hurdles.
But there may be more at play that explains some of the hesitancy. Some firms are still unclear as to what exactly is involved and what artificial intelligence does, and that can lead to concerns. How will the surveillance be used? How will it impact us? How will it affect our customers? How will we explain outputs and feature based explanations to regulators? You must get beyond the sci-fi feel of it and remember that it’s just a collection of advanced calculations that boil down to making predictions by leveraging all data sets to get a 360o view. The same data that applies to AML prevention may also lead to higher productivity or surprising new ways to serve the customer.
By embracing AI and joining forces with the right technology partner, you can lower AML risk by making it easier to spot suspicious behavior, reduce false negatives positives and trace the life of the crime. Modern machine learning technology also helps you improve the business speed to market and elevate customer service by reducing friction all from one unified platform.
* Source: Chartis
** Source: LexisNexis Risk Solutions