The field of machine learning involves the application of artificial intelligence, or AI, to create a system that gives  computers the ability to “learn,” or improve automatically without being explicitly programmed or aided by human intervention. Data is an important part of the equation when it comes to the advancement of a system’s machine learning capabilities. With a larger data set, systems become more intelligent, and thus more proficient at understanding complex patterns and relationships.

Machine learning applications allow their human users to handle massive volumes of data that would otherwise take a great deal of time and money to collect, manage, and analyze. When you think about what this technology is capable of, you might be able to envision how it’s used for disciplines like medical science and engineering. But nowadays, it’s not uncommon for financial institutions—even those on the smaller side—to use machine learning technologies in their operations, including those for anti-money laundering (AML) compliance.

If you are a decision-maker in a bank and you’re curious about how machine learning technologies can benefit your AML compliance strategy, here are some important resources for you. These will illustrate how machine learning-based compliance AML applications can safeguard your bank’s assets and help your bank accomplish the task of AML compliance.

The Relationship of Machine Learning and AML Compliance Approaches

At the outset, artificial intelligence and machine learning applications are highly advantageous banks, especially for tasks as complex and data-heavy as AML compliance. The use of machine learning in an AML solution can enhance a bank’s data collection and analytics abilities for know your customer (KYC) and customer due diligence (CDD) operations. In this regard, machine learning allows banks to sift through their KYC and CDD data more quickly and accurately. This, in turn, helps them flag any imminent risks of financial crime and implement appropriate protective measures.

But it’s important to note that machine learning is no magic bullet for the difficult task of AML compliance. The bank’s safety from financial crime also depends on what kind of AML approach the machine learning technologies are utilized for. Banks may have upgraded their tech stack for AML compliance to include machine learning capabilities, but if their approach is still dependent on flagging single transactions and marking false positives one by one, their compliance team may not be as productive or as empowered as they should be. Even if machine learning allows staff to become faster at sorting and flagging individual cases, compliance teams will still be stuck on evaluating threats on a piecemeal basis. This will still buy financial criminals some time to circumvent the bank’s AML safety measures. A bank may be exposed to scandal or liability, and its customers’ finances may still be compromised, because the technology wasn’t paired with the right AML approach. 

New Perspectives in AML Compliance: Using Machine Learning in Pattern-Based Approaches

Luckily, some banks have learned how to use machine learning for a more effective approach to AML compliance: that of pattern-based thinking. Pattern-based thinking eschews the single-transaction approach traditionally used by banks for their KYC and CDD policies, and instead, uses machine learning to identify patterns in customer behavior. This shift often results in less time and labor wasted on individual false positives and more centralized KYC and CDD data. .

Through this 360-degree vantage point of their customer data, banks may be able to pinpoint the suspicious clusters or webs of behavior that are usually indicators of financial crime. Machine learning can help banks interpret large amounts of data from particular time periods, or involving particular transaction amounts. One concrete application is the use of artificial intelligence and machine learning to graph cases and to identify connected entities. Graph analytics involving machine learning technologies may be able to spot a phone number shared by multiple registered customers, for example. The compliance team may then also be alerted to the fact that these same customers regularly send large amounts of money to each other.

In the use case mentioned above, machine learning allows a bank’s compliance team to derive conclusions based on logical patterns—not just individual cases, where the full context is not yet available. This is a more dynamic approach to conducting AML—and ultimately, the approach that will maximize the use of machine learning technologies.

It will already be a big step for your bank if you choose to onboard an AML solution with machine learning capabilities. But take this opportunity not only to innovate your tech stack, but your actual AML compliance strategy.

Go beyond the rules-based approach and a case-by-case methodology, and use your machine learning technologies to bring noteworthy patterns to the surface. This will unlock the technology’s full potential and ensure that it serves its primary purpose: to keep your bank safe and free from financial crime. 

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