Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Finance managers, controllers, and CFOs
• Management and financial accountants
• Internal and external auditors
• Financial analysts and planning professionals
• Risk, compliance, and governance officers in finance

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants will be able to:

• Understand AI concepts relevant to accounting, finance, and auditing
• Identify high-impact AI applications in financial reporting, analysis, and operations
• Evaluate and implement AI-driven tools for automation, forecasting, and fraud detection
• Mitigate ethical, data privacy, and governance risks in AI adoption
• Collaborate effectively with IT and data teams to deliver AI-powered financial insights

DAY 1

Introduction to AI for Finance and Accounting

• Welcome and introduction
• Pre-test
• Key AI concepts: machine learning, robotic process automation (RPA), NLP, predictive analytics
• The role of AI in finance: efficiency, insights, agility
• Overview of tools and platforms used in finance AI (Power BI, Alteryx, Python, SAP AI modules)
• Case studies
• Group discussion

DAY 2

AI Applications in Financial Operations

• AI-powered automation of accounts payable/receivable, payroll, and reconciliations
• Intelligent invoice processing and cash flow optimization
• AI in budgeting, forecasting, and financial modeling
• Using AI to identify anomalies and reduce operational errors
• Workshop

DAY 3

AI in Financial Reporting, Auditing, and Compliance

• Automating financial close and reporting cycles with AI
• Continuous auditing and real-time risk detection using AI
• Fraud detection and internal control monitoring with machine learning
• AI for regulatory compliance and risk analytics (e.g., SOX, AML)
• Exercise

DAY 4

Governance, Ethics, and Risk in Financial AI

• Ethics and bias in AI algorithms and financial models
• Ensuring data privacy, integrity, and explainability
• Regulatory considerations: GDPR, AI risk frameworks, financial regulations
• Governance frameworks and audit trails for AI models
• Case study

DAY 5

Strategic AI Adoption and the Future of Finance

• Building an AI strategy aligned with financial and business goals
• Talent, change management, and upskilling in finance teams
• Collaboration between finance, IT, and data science
• Emerging technologies: Generative AI for reporting, AI + blockchain for audit
• Final group project
• Post-test
• Certificate ceremony