Machine Learning in Risk Management: Money Laundering and Fraud Detection
Current Situation
Machine learning has come to play an important role in the banking and finance industry, thanks to unprecedented growth in the availability of computing power, data storage, and algorithms. As many banks are increasingly implementing and employing such technologies, machine learning has the potential to change many traditional banking processes.
In order to properly assess and keep up with these developments, banking professionals need a basic understanding of the underlying machine learning algorithms.
Objective
In this Master Class, we discuss some of the most important machine learning tasks with cases in risk management.
Topics include an introduction to the relevant machine learning algorithms and to performance metrics, classification in money laundering control, and outlier detection in fraud prevention.
While coding skills are not a prerequisite, participants will get some hands-on coding experience during the Class.
Target Audience
This Master Class is aimed at financial industry practitioners who work in risk management or compliance or who are involved in machine learning projects. At the same time, the course is designed to provide all interested parties valuable insights into the world of machine learning, and to offer some initial hands-on coding experience.
SAQ Recertification
This Master Class is an acknowledged SAQ recertification measure for the CWMA, CCoB, Affluent-, SME-, and Individual Client Advisor profile and comprises four learning hours.