Improving Traditional Anti-Money Laundering Platforms with Machine Learning

Banking CIO Outlook Article

In this Banking CIO Outlook article Tim Mueller, managing director, and Amin Ahmadi, associate director, discuss how to improve traditional anti-money laundering (AML) platforms with machine learning.

Anti-money laundering transaction monitoring (TM) is based on the premise that if financial institutions apply appropriately designed rules to financial transaction activities, they can identify patterns and/or activities that represent potentially suspicious behavior. Recently, however, better coverage of AML risk indicators through expanded rules and increased regulatory oversight have significantly increased the volume of nonproductive and productive alerts generated by TM systems. It has thus become more challenging to effectively separate the nonproductive alerts from those representing suspicious activity, and to disposition the alerts efficiently.

Improving the efficiency of rules and introducing automation to the alert disposition process are two ways to meet these challenges. Such improvements, however, must be transparent and defensible under compliance department and regulatory scrutiny, as well as flexible and scalable for institutions with different customer and transaction footprints.

Fortunately, these requirements can be satisfied by artificial intelligence and machine learning (AI/ML), which have the capacity to train a set of algorithms on some representative data, in order to make intelligent inferences about the current or future data. In this article, we discuss application of ML/AI in two areas of transaction monitoring: rule tuning and alert prioritization.

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The goal of parameter tuning is to determine a threshold which separates the 'normal' transactional behavior from 'suspicious' behavior.

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