Detecting Outliers Using Data Analytics
Significance of Outliers - Demystified
When testing transactions, whether they are vendor invoices, GL postings, or travel & entertainment expenses. Use of examples using Arbutus Analyzer demonstrates the best analytics for identifying outliers.
- What are outliers?
- Why are they important?
- Traditional outlier identification
- Advanced statistical methods
Speaker: Michael Kano
ACDA, Data Analytics Consultant - Arbutus Analytics
Highly skilled data analytics professional with more than twenty years of international experience in the development of ACL/Arbutus data analysis programs (scripts) for auditors, fraud detection professionals, SOX compliance teams, and business departments. Drives strategic integration of data analytics into audit programs. Identifies areas suitable for testing in order to expand audit scope and realize productivity gains, including continuous monitoring. Communicates well with business process owners in functional areas to secure buy-in for testing support. Provides user training and best practices development and documentation.
Arbutus, in partnership with AuditNet, presents series of engaging webinars with live Q&A. Insightful demonstrations and informative content presented by Arbutus Customers, Consultants and Data Analytics subject matter experts.