This is an archived session with Angela Kroboth – Data Analytics Expert, Next Step Data Insights – from the Auditopia 2026 Arbutus Conference.
Audit teams are increasingly hearing about AI and machine learning, but leaders still need analytics results they can understand, defend, and act on. This session will compare traditional audit analytics with AI / ML approaches and explain when each is the right tool. We will discuss practical techniques such as regression, clustering, outlier detection, and Benford’s Law, with an emphasis on how to translate results into risk, control, fraud, and compliance language for leadership.
Learning Objectives
Participants will be able to:
- Distinguish when traditional audit analytics are more appropriate than AI / ML, and when AI / ML can add value.
- Identify practical audit use cases for regression, clustering, outlier detection, and related techniques.
- Explain AI / ML results to audit leadership in terms of risk, controls, exceptions, uncertainty, false positives, and business impact.
Field of Study: Computer Software & Applications (1 CPE) ~ Program Knowledge: Intermediate ~ Duration: 60 min


ACL Customer Success Stories (1.2 CPE) 
