Programme Parameters
Advanced Postgrad
8 Weeks (80 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Auditing and Compliance shows how machine learning strengthens assurance and control in a data-heavy profession. You learn to apply AI to audit and compliance tasks: detecting anomalies and fraud across full populations rather than samples, enabling continuous auditing, assessing risk, and monitoring regulatory compliance. The course keeps professional standards central — auditability, explainability, evidence and independence — because in assurance the how matters as much as the result. You finish able to reason about applying AI to audit and compliance responsibly. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in auditing and compliance — anomaly and fraud detection, continuous auditing, risk assessment and regulatory compliance with auditability.
📋 Course Objectives
1. Detect anomalies and fraud across full data populations.
2. Enable continuous auditing with AI.
3. Assess and prioritise risk.
4. Monitor regulatory compliance.
5. Preserve auditability, evidence and independence.
👥 Who Should Enroll?
• Auditors and compliance professionals
• Risk and assurance teams
• Fintech and audit-technology staff
• Students of accounting and audit
🚀 Key Learning Outcomes
• An understanding of AI in audit and compliance.
• A continuous-auditing perspective.
• A standards-first professional approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Where Analytics Fits the Audit
• 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
Journal Entry and Transaction Analytics
• 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
Extraction and Contract Review
• Document extraction from invoices, contracts and confirmations
• Obligation and clause identification for compliance review
• Verification workflows where extraction error is the auditor's risk
Monitoring and Controls Testing
• 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
Documenting AI-Assisted Work
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Westlaw |
| Covered Tool / Platform | LexisNexis |
| Covered Tool / Platform | Microsoft Office |
| Covered Tool / Platform | AI Legal Tools |
| Covered Tool / Platform | Contract Analysis Software |
Frequently Asked Questions
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