Expression of Interest (EOI) for AI Faculties for AI Certificate Course
The Institute of Chartered Accountants of India (ICAI) invites experienced and knowledgeable AI professionals to express their interest in serving as faculty members for the upcoming "AI for Chartered Accountants (AICA)-Level 1" certification course. This initiative aims to equip Chartered Accountants with foundational AI skills, enhancing their proficiency in applying AI technologies within the realms of finance, audit, and industry practices.
Course Overview:
- Course Title: AI for Chartered Accountants (AICA)-Level 1
- Participants: Chartered Accountants
- Pre-Requisites: Basic knowledge of computer operations, Microsoft Office, finance, audit, statistics, and problem-solving skills.
- Objective: To develop a foundational understanding of AI concepts, enhance proficiency in AI tools relevant to practitioners and industry professionals, and build AI skills for effective application in professional practices.
- Duration: 18 hours (6 hours/day over 3 days - Friday, Saturday, and Sunday)
- Mode: Physical Classroom
Teaching and Assessment Methods:
- Teaching Methods: PPTs, Lectures, interactive discussions, practical exercises, hands-on sessions, capstone project, group projects, and case study analyses.
- Assessment Methods: Quizzes, assignments, group project presentations, and a final exam.
Course Modules:
| Day | Module | Particular Details | Duration (hours) |
|---|---|---|---|
| Day-1 | Module 1 | Overview of AI & Basic Concepts Overview of AI, Block Chain, Cloud Computing, Dx for Accounting, Ethics, Robotic Process Automation (RPA), Internet of things (IoT), World Wide Web. |
1.5 |
| Day-1 | Module 2 | Introduction to AI Overview of Digital Transformation and AI. Overview of emerging technologies. How digital changes will impact chartered accountants. Evolution of AI in professional fields (e.g., machine learning, natural language processing). Prompt Engineering. |
1.5 |
| Day-1 | Module 3 | Dx for Accounting Tools and techniques for data analysis in Finance & Audit. Case studies on data-driven decision-making. Hands-on exercise with a popular Dx for Accounting tool. AI Use Cases for Members in Practice & Industry. |
3 |
| Day-2 | Module 4 | Machine Learning Basics Understanding data types and Structure. Introduction to Machine Learning Concepts. Basic algorithms relevant to Finance & Audit. |
2 |
| Day-2 | Module 5 | AI and ML application in Finance Practical applications of AI & ML Concepts in the Finance Sector. Reporting and Dashboard. Fraud detection through pattern recognition. Predictive analytics in Financial Forecasting. |
2 |
| Day-2 | Module 6 | Natural Language Processing (NLP) in Finance & Audit Generative AI in Finance & Audit. Understanding NLP and its applications in Finance & Audit. Automated document analysis and report generation. Case study: AI-driven tax and regulatory updates. Practical use cases and demonstrations. Automation of Transactional processes using AI. |
2 |
| Day-3 | Module 7 | Ethical Considerations and Future Trends Ethical issues and governance in AI applications. Responsible AI in alignment with government guidelines. Future trends in AI and Finance & Audit. Recap and examination. |
1 |
| Day-3 | Module 8 | Implementing AI Projects in Finance & Audit Steps for starting AI projects in finance. Managing AI project lifecycle: from planning to evaluation. Group project: Designing a small AI solution for Finance & Audit problem. |
2.5 |
| Day-3 | Module 9 | AI in Auditing & Tax AI tools for risk assessment and compliance. Continuous auditing and real-time reporting systems. Ethics and reliability of automated systems risk management guidelines. Include ISO 42001 for AI in business society. Consider ISO 23894:2023 for AI risk. Practical use cases and demonstrations. |
2.5 |
Faculty Requirements:
- Expertise: Deep knowledge and practical experience in AI, machine learning, NLP, data analysis, and related areas.
- Engagement: Ability to engage with chartered accountants and industry professionals, facilitating practical and theoretical learning.
- Commitment: Availability for the full duration of the course and participation in preparatory activities.
Application Process:
Interested candidates are invited to submit their Basic Details, Experience, Educational Qualifications, Technical Experience, Relevant Teaching Experience (Number of Years), Teaching Experience in ICAI (Number of Years), Research paper (Optional) and AI Articles (optional) highlighting their expertise in AI and related fields. Further, faculty is also required to attach a cover letter along with a detailed resume in PDF Document.
Submission Details:
Website: Click Here
Deadline: 30th September, 2024