Behavior Monitoring levels up results in AML Compliance solution

Behavior Monitoring beats Transaction Monitoring for peak Risk Management in AML Compliance solutions

By Frank Cummings, CEO

How effectively do we fight financial crime? Currently, we tend to look for a very small number of illicit behaviors known to predict criminal activity. This approach limits us to only really looking backward for crimes. But what if there were a better way to identify financial crime?

In my experience, it’s always about the behavior. And combining today’s analytics with our shared understanding of money-related behaviors that predict crime would be game-changing—extraordinarily game-changing if our goal is to fight financial crime. 

What exactly does it mean to lead with analysis of behavior? What would that look like for financial institutions?

The first part of this type of behavioral analysis would always be a full and complete CDD/KYC collection, including a total risk view. Why start with CDD/KYC? Because it provides prime data for predictive behavioral analytics.

Some institutions collect over 600 data elements during their onboarding process for each customer. Each of these data elements can be used in deep dives into behavioral analytics. 

Imagine looking down into a deep clear well to identify a drop of cloudy water amidst all the clear water. Searching for suspicious behaviors is a bit like that—an almost impossible challenge. But if you can eliminate everything that makes sense and aligns with the predictable, what’s left is probably what you are looking for. 

With today’s analytics, we can quickly and easily identify and compare peer-alike normal behaviors and then flag the abnormal. This is the essence of behavior monitoring, i.e. looking for differences in how each customer conducts its transactional activity–its Behavior—in comparison with its peers. 

This is a simplification of the process, but I think you get the idea: We can look at every data element and compare it against every customer we have with that same data element. Building out that analysis over time and volume creates a library, so to speak, of predictable peer-alike behaviors that can accurately predict a range of normal business dealings. Behaviors for peer-alike businesses that fall outside those norms would stand out and warrant enhanced due diligence.

Part of the key to achieving this type of behavior monitoring is how we look at these elements and how we progress through a matrix of these data points looking for what is different–through every combination possible. We only want to know when a customer changes their expected behavior and why, on an ongoing basis. Through these behavioral analytics, we can flag unexpected behaviors relative to peers—and if the behavior cannot be explained, we have possible suspicious activity.

The whole premise of the idea is this: Why look for just a few indications of possible crimes when you can look for them all by looking for and understanding deviations from peer-alike expected activity.

Once your AML system understands peer-alike expected behaviors for each customer type, based on the history of your entire customers tractional behavior, the ongoing analysis would focus on automated monitoring. Armed with your predictions of normal behaviors, you need only focus on unexpected activity as you feed every transaction into the system.  And with today’s technology, you can do this in real time and catch fraud or other nasty things, as well.  

This system of analytics and prediction does not change the face of financial crime. But this proactive approach to behavior monitoring might reveal to you many faces of the criminals attempting to use your institution for their criminal activity. 

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