Machine Learning in Risk Management Part 1: Supervised Learning
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
IIn this Master Class, we discuss some of the most important machine learning tasks in risk management. Topics include an introduction to the relevant machine learning algorithms and performance metrics, and to their concrete application in the context of money laundering control and fraud detection. While coding skills are not a prerequisite, participants will get some hands-on coding experience by applying a case study in groups and, accompanied by experts reflecting on the results.
"Machine Learning in Risk Management Part 1" will focus on supervised machine learning techniques. In 2022, Part 2 will spotlight unsupervised learning. Each Part is designed in a such a way that interested parties can choose to attend either or both of the two offerings.
Target Group
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 SFI Master Class is an acknowledged SAQ recertification measure for CWMA, CCoB, Affluent-, SME-, and Individual Client Advisor profiles and comprises four learning hours (4 credits).