Methods in Audit Data Analytics: Case Studies

In this course, participants examine real-world case studies demonstrating how various techniques are used to detect fraud, misuse, inefficiencies, and recover funds. The course covers data integrity, data quality, and data integration, and will familiarize learners with identifying structured vs. unstructured data, intersecting data, and developing a systematic process of analysis. Learners will learn how to efficiently extract business insights from data, visually communicate those insights to their stakeholders, and apply these techniques to audit plans, tests, and other audit components. The course introduces learners to artificial intelligence, machine learning, the rise of quantum computing, and how these will impact the future of auditing.

Topics covered include a historical perspective to data analytics, modern tools, understanding the structure of flat data files, and understanding the audit perspective. This course also includes finding fraud, predictive analytics, and accelerated learning techniques.

Who should attend:

Compliance Officers, Audit Managers and Directors, Financial, Operational, and IT Auditors, and key operational personnel.

Here are the topics we'll cover.

1. Course Overview
2. Introducing Case Studies for Data Analytics