📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI in Auditing and Compliance, from foundations to a certified capstone project.
Audit Context
• The audit assertion model and mapping analytics to assertions
• ISA 500 evidence expectations applied to machine-generated findings
• Full-population testing versus sampling, and what changes in the file
Testing
• Journal entry testing for fraud indicators and unusual postings
• Benford analysis and its frequent misapplication
• Three-way match, duplicate payment and cut-off testing at population scale
Documents
• Document extraction from invoices, contracts and confirmations
• Obligation and clause identification for compliance review
• Verification workflows where extraction error is the auditor's risk
Continuous
• Continuous controls monitoring and exception management
• Segregation of duties and access analytics on ERP data
• Tuning exception thresholds so the second line is not drowned
Evidence
• Documenting model logic, inputs and limitations in the audit file
• Reviewer competence and the risk of unexamined automation bias
• Regulator and inspection expectations for technology-assisted audit
Earn government-registered certification in AI in Auditing and Compliance
e-Certificate and e-Marksheet issued on successful completion.
