Insights: Applying Machine Learning to AML Transaction Monitoring

In this video-based course, Kiran Kumar Shah, Head - AML/CFT, Kumari Bank Nepal provides an insight into concepts such as data science and machine learning, which are often misconstrued terms.
Many financial and non-financial institutions are currently using Rule Based Transaction Monitoring which involves building different scenarios to identify red flags of money laundering. However, this method of detecting money laundering can be ineffective in the face of changing money laundering trends and innovative ways used by criminals to circumvent controls. Machine learning algorithms, on the other hand, can help reduce time and effort of analysts by detecting patterns in the thousands of transactions performed by a customer and match them with the customer profile, leading to more true positive alerts.
In the course, Kiran starts with a theoretical background and then moves on to demonstrating a practical example of the application of a machine learning algorithm in transaction monitoring. AML compliance professionals will find it useful to know and understand about tools such as data analysis and visualisation and leverage them for the benefit of their organisation.