Machine Learning application in Internal Audit

Machine Learning (ML) is revolutionizing various industries, including internal audit, by providing advanced analytical capabilities to extract insights from large datasets. This course focuses on the application of ML techniques specifically tailored to the context of internal audit functions. Participants will learn how to exploit the power of ML algorithms to enhance risk assessment, detect anomalies, and optimize audit processes. Through practical examples and hands-on exercises, attendees will gain the necessary skills to leverage ML effectively in internal audit activities.

Learning Objectives:

Participants who successfully complete this course will be able to:

• Understand the fundamentals of Machine Learning, explore various Machine
• Learning algorithms and their applications in risk assessment, anomaly detection, and audit optimization.
• Learn how to collect, preprocess, and analyze data for Machine Learning applications in internal audit.
• Gain proficiency in implementing supervised and unsupervised ML techniques to identify patterns and outliers in audit data.
• Gain insights into leveraging emerging technologies such as artificial intelligence and natural language processing in internal audit activities.
• Develop strategies for addressing data privacy and security concerns when implementing ML in internal audit.