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
Ethical and Regulatory Aspects of AI in Accounting addresses the responsibilities that come with automating financial judgement. You examine where AI is used in accounting and audit — anomaly detection, automated reporting, risk assessment — and the ethical and regulatory questions each raises: auditability and explainability, bias, data integrity, professional accountability and independence. The course maps the compliance landscape and professional standards that govern financial AI, and the governance practices that keep it trustworthy. You finish able to help deploy AI in accounting in a compliant, ethical way. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers the ethics and regulation of AI in accounting and finance — governance, auditability, bias, transparency and compliance for AI in financial reporting and audit.
📋 Course Objectives
1. Identify where AI is used in accounting and audit.
2. Assess auditability, explainability and data integrity.
3. Address bias and professional accountability.
4. Map relevant regulation and professional standards.
5. Apply governance to financial AI.
👥 Who Should Enroll?
• Accountants, auditors and finance professionals
• Risk and compliance staff
• Fintech and audit-technology teams
• Students of accounting and AI ethics
🚀 Key Learning Outcomes
• The ability to evaluate AI in accounting for compliance.
• A governance approach for financial AI.
• An ethics-first professional perspective.
• 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
Ethics Codes Meet Automation
• IESBA fundamental principles applied to AI-assisted accounting work
• Professional competence when the practitioner cannot fully inspect the tool
• Objectivity and self-review threats introduced by vendor-supplied models
Financial Reporting and Assurance Boundaries
• Estimates and judgements produced with model assistance, and their disclosure
• Auditability of AI-derived figures in the financial statements
• Where automation crosses from bookkeeping into professional judgement
The Compliance Perimeter
• EU AI Act obligations reaching financial and accounting functions
• Data protection duties over client financial data, including cross-border transfer
• Records retention and the evidential status of automated workings
Bias, Error and Accountability
• Bias in credit, pricing and classification models touching accounting outcomes
• Allocating responsibility between preparer, reviewer and vendor
• Client communication about AI use in the engagement
Building a Defensible Policy
• Firm-level AI use policy: permitted uses, prohibited uses, review requirements
• Vendor due diligence questions with real diagnostic value
• Training and supervision obligations for junior staff using these tools
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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