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5 steps to implementing AI for AML compliance – Part 1

By 2030, artificial intelligence (AI) will save the banking industry more than $1trn, according to analysts. Of that vast sum, it is expected that banks and credit unions will save $217bn—simply by applying AI to their compliance and authentication practices, and to other forms of data processing.

I recently reviewed the first steps that financial institutions can take to transform their AML compliance and fraud prevention programs, augmenting their rules-based systems with AI-driven models. This includes implementing advanced anomaly-detection models and transaction monitoring, with machine learning and robotic process automation.

Once you’ve decided how to evolve your program, the next step is to identify AML and/or fraud use cases, as well as any supporting data ingested by the AI system. This becomes the foundational layer from which the AI can uncover new patterns and increase the accuracy of its detection capability.

In my previous article, I spoke of how technology solution providers don’t “program” the AI system to detect threats; they help financial institutions train their systems to do this. This requires subject matter experts and thought leaders in the AML compliance and fraud space to work with the AI system—most especially during the training process—to determine whether seemingly abnormal behaviours or clusters of transactions are suspicious or fraudulent.

Unfortunately, there isn’t one model that works for all organizations, which can make the training process take more time than might be expected. When beginning the AI journey, keep in mind that there are many steps, and most require human intervention. Once you’ve finished your training, you’ll be closer to having a solution that integrates your data and meets your AML and fraud detection and prevention needs.

 

Figure 1: Steps to get started on adding AI-based capabilities to an AML or fraud detection program

Exploratory analysis

To begin developing an AI-driven AML or fraud detection program, users first perform an exploratory analysis to examine supporting data and reveal existing patterns.

After identifying potential patterns, our data scientists guide our clients to help them understand and interpret exactly what they are seeing. This is done through a series of questions such as, “Are these supposed to be here?” and “Are these graphs supposed to be this steep?” The answers to these questions will then guide the next phase of the program.

I will review the next phases in Part 2 of this blog. Stay tuned!

 

 

About Khaled Ghadban

Khaled Ghadban (LinkedIn) is Director of Analytics and Data Science at CaseWare RCM and has more than 20 years of experience in the data analytics space in various industries, including financial services. He is a regular contributor to conferences and a frequent speaker on the topics of analytics and cognitive (AI).