Practical AI for Finance and Accounting (Financial Analysis, Reporting & Automation)

Finance Managers, Accountants, Financial Analysts, FP&A Professionals, Budgeting and Forecasting Teams, Management Reporting Professionals, Cost and Performance Analysts, Finance Business Partners, and professionals involved in financial planning, analysis, reporting, and decision support.

Participants should have a basic understanding of finance and accounting concepts and general familiarity with Microsoft Excel. No programming knowledge or previous AI experience is required.

• Module 1: AI in the Modern Finance and Accounting Function
  • Understanding generative AI and its practical capabilities
  • Applications of AI in finance and accounting
  • Selecting suitable AI tools for different tasks
  • Understanding context, instructions, and AI-generated responses
  • Identifying opportunities to improve productivity and decision support
  • Recognizing limitations, hallucinations, and financial accuracy risks
  • Determining where professional judgment and human review are essential
  • Responsible handling of financial and confidential information
• Module 2: Effective Prompting for Finance Professionals
  • Principles of effective prompting
  • Structuring prompts for financial and accounting tasks
  • Providing relevant context, assumptions, and constraints
  • Specifying output formats, tables, and reporting requirements
  • Role-based and example-based prompting
  • Breaking complex tasks into manageable steps
  • Requesting explanations of calculations and assumptions
  • Reviewing, challenging, and refining AI-generated outputs
  • Building reusable prompt templates
• Module 3: Working with Financial Documents and Information
  • Reviewing financial reports and accounting documentation
  • Extracting relevant information from lengthy documents
  • Summarizing financial statements and management reports
  • Comparing documents and reporting periods
  • Identifying key observations, exceptions, and inconsistencies
  • Extracting tables, assumptions, risks, and action points
  • Reviewing finance policies and procedures
  • Preparing concise executive summaries
• Module 4: AI-Assisted Financial Statement Analysis
  • Interpreting income statements
  • Analyzing balance sheets
  • Reviewing cash flow statements
  • Supporting ratio and performance analysis
  • Comparing financial results across periods
  • Identifying trends and unusual movements
  • Highlighting potential areas for further investigation
  • Converting financial observations into business insights
  • Validating calculations and AI-generated interpretations
• Module 5: Variance, Trend and KPI Analysis
  • Actual versus budget analysis
  • Actual versus forecast analysis
  • Period-over-period performance comparisons
  • Identifying significant financial deviations
  • Investigating potential variance drivers
  • Interpreting trends and performance patterns
  • Analyzing financial and operational KPIs
  • Preparing clear variance explanations and performance commentary
  • Developing actionable management insights
• Module 6: Budgeting, Forecasting and Scenario Analysis
  • Using AI to support budgeting and financial planning
  • Structuring and documenting budget assumptions
  • Reviewing historical performance and trends
  • Supporting forecast preparation
  • Developing base, optimistic, and downside scenarios
  • Conducting sensitivity and what-if analysis
  • Evaluating the financial implications of changing assumptions
  • Challenging planning assumptions and identifying information gaps
  • Preparing forecast narratives for management
• Module 7: AI, Excel and Financial Data
  • Using AI alongside Microsoft Excel
  • Generating and explaining formulas for common finance calculations
  • Troubleshooting formulas and spreadsheet logic
  • Structuring financial datasets for analysis
  • Supporting data cleaning and transformation
  • Designing financial analysis templates
  • Supporting PivotTable and analytical workflows
  • Explaining complex spreadsheet models
  • Identifying potential spreadsheet issues and validating results
• Module 8: Management Reporting and Financial Communication
  • Transforming financial analysis into management reports
  • Writing clear and concise financial commentary
  • Preparing monthly and quarterly performance narratives
  • Summarizing financial results for senior management
  • Translating technical financial information into business language
  • Creating executive summaries
  • Highlighting risks, opportunities, and required actions
  • Tailoring reports for different stakeholders
  • Preparing presentation-ready financial insights
• Module 9: Automating Repetitive Finance and Accounting Workflows
  • Identifying repetitive and time-consuming activities
  • Moving from individual prompts to structured workflows
  • Creating reusable prompt sequences
  • Standardizing recurring financial analysis
  • Supporting month-end reporting activities
  • Preparing recurring variance and KPI commentary
  • Structuring document comparison and information extraction workflows
  • Supporting reconciliation and exception-review activities
  • Creating standardized reporting templates
  • Introduction to AI agents and agent-assisted workflows
  • Assessing integration requirements and human review checkpoints
  • Determining which activities are suitable for automation
• Module 10: Governance, Risk and Responsible AI
  • Protecting financial data and confidential information
  • Understanding AI-generated errors and hallucinations
  • Applying verification and validation techniques
  • Checking calculations, assumptions, and source information
  • Maintaining auditability and traceability
  • Establishing human review and approval checkpoints
  • Managing access and appropriate use of AI tools
  • Developing practical internal guidelines for AI in finance and accounting