AI-Assisted Financial Modelling & Decision Support Training Workshop

Course overview

The AI-Assisted Financial Modelling & Decision Support Training Workshop equips finance professionals with practical skills to integrate artificial intelligence into financial analysis, budgeting, forecasting, investment evaluation, risk assessment, and management reporting. The programme explores how generative AI assistants, AI-enabled spreadsheet tools, statistical software, and business intelligence platforms can improve analytical productivity and support more informed financial decisions.

Participants will learn to use AI to assist with financial data preparation, formula development, financial statement analysis, budget variance interpretation, scenario modelling, cash flow forecasting, investment appraisal, financial risk analysis, and the preparation of management reports. The workshop combines AI-assisted workflows with established financial modelling methods to ensure that calculations, assumptions, and conclusions are independently checked.

Course objectives

• Apply AI to financial analysis: Use approved AI assistants to support financial data interpretation, spreadsheet preparation, formula development, financial summaries, and analytical workflows.
• Validate AI-generated financial outputs: Identify calculation errors, unsupported assumptions, misleading interpretations, confidentiality risks, and other limitations through independent verification, documentation, and human review.
• Integrate statistical analysis and business intelligence: Use SPSS and Stata for suitable statistical and econometric analyses and Power BI for interactive financi Develop AI-assisted financial models:al dashboards, reporting, and communicating insights.
• Improve financial decision support: Apply AI-assisted scenario analysis, budget variance analysis, sensitivity testing, and financial performance interpretation to evaluate alternative business decisions.
• Validate AI-generated financial outputs: Identify calculation errors, unsupported assumptions, misleading interpretations, confidentiality risks, and other limitations through independent verification, documentation, and human review. 4. Course Modules
• Integrate statistical analysis and business intelligence: Use SPSS and Stata for suitable statistical and econometric analyses and Power BI for interactive financial dashboards, reporting, and communicating insights.

Who should attend

Course content

• Module 1: Foundations of AI in Financial Modelling and Decision Support Introduction to artificial intelligence in finance, generative AI and large language models, AI-assisted versus conventional financial modelling, applications in banking and corporat
• Module 2: AI Tools and the Modern Financial Analyst's Toolkit Introduction to Microsoft Copilot and other approved generative AI assistants, AI-enabled spreadsheet capabilities, natural-language interaction with financial data, AI-supported document anal
• Module 3: Financial Data Preparation and Quality Assurance Using AI Preparing financial datasets, identifying missing values and inconsistencies, standardising reporting periods, identifying duplicate records, explaining unusual observations, suggesting
• Module 4: AI-Assisted Excel Formulas and Financial Calculations Using AI to draft and explain Excel formulas, lookup functions, conditional calculations, aggregation formulas, financial functions, dynamic formulas, error-handling techniques, and formula
• Module 5: AI-Assisted Financial Statement Analysis Analysing income statements, balance sheets, and cash flow statements, calculating financial ratios, examining profitability and liquidity, evaluating leverage and operating efficiency, identifying signi
• Module 6: AI-Assisted Budgeting, Forecasting and FP&A Developing operating budgets, revenue forecasts, expenditure projections, rolling forecasts, headcount budgets, working capital forecasts, and financial planning models. Using AI to assist with foreca
• Module 7: AI-Assisted Integrated Financial Modelling Developing linked income statements, balance sheets, and cash flow statements, building revenue and cost drivers, modelling working capital, depreciation, capital expenditure, taxes, debt, and equity,
• Module 8: AI-Assisted Cash Flow and Liquidity Analysis Forecasting cash inflows and outflows, projecting working capital requirements, examining liquidity ratios, identifying potential cash shortages, analysing receivables and payables, modelling alterna
• Module 9: AI-Assisted Investment Appraisal and Business Valuation Applying AI to assist with investment evaluation, discounted cash flow (DCF) modelling, net present value (NPV), internal rate of return (IRR), payback calculations, valuation assumptions,
• Module 10: AI-Assisted Scenario Analysis and Financial Decision Modelling Building base-case, optimistic, and pessimistic scenarios, changing revenue and cost assumptions, evaluating interest-rate and foreign-exchange shocks, examining changes in sales v
• Module 11: AI for Financial Risk Analysis and Decision Support Identifying financial risks, analysing liquidity and solvency indicators, examining credit portfolio trends, assessing exposure to interest-rate and foreign-exchange changes, interpreting ris
• Module 12: AI-Assisted Statistical Analysis Using SPSS and Stata Using AI to help formulate statistical questions, suggest appropriate analytical procedures, explain statistical outputs, draft analysis plans, interpret regression results, examine financi
• Module 13: AI-Assisted Financial Dashboards and Reporting with Power BI Preparing financial data for reporting, developing financial KPIs, designing management dashboards, comparing budget and actual performance, visualising revenue and expenditure trend
• Module 14: AI-Assisted Financial Reporting, Research and Executive Briefings Preparing financial commentary, management reports, investment summaries, board briefing materials, variance explanations, executive presentations, and decision memos. Using AI
• Module 15: AI Governance, Financial Data Security and Model Validation Responsible use of AI in financial workflows, confidential and personally identifiable financial data, approved tools and access controls, human oversight, model transparency, bias, h
• Module 16: Integrated AI-Assisted Financial Modelling Capstone Participants will work through a realistic financial case requiring them to clean financial data, analyse performance, develop a financial forecast, build a budget or cash flow model, evaluat

General notes

• Practical demonstrations will use Microsoft Excel, AI assistants such as Microsoft Copilot and other approved generative AI tools, IBM SPSS Statistics, Stata, and Microsoft Power BI. Excel and AI assistants support financial modelling and analysis, SPSS and Stata provide statistical and econometric analysis where appropriate, while Power BI supports interactive dashboards and management reporting.
• The programme also addresses responsible AI use, confidentiality of financial information, verification of AI-generated outputs, model transparency, bias, and human oversight. These topics reflect established approaches to AI risk management and emerging AI-enabled finance workflows
• Scenario 1: AI-Assisted Corporate Budget Analysis Use AI to help review a company's budget and actual expenditure, identify material variances, draft possible explanations, verify the calculations in Excel, and prepare a management summary.
• Scenario 2: Financial Forecasting and Cash Flow Planning Use historical revenue, expenditure, and cash flow data to develop a forecast, compare alternative scenarios, identify potential liquidity gaps, and present the results in a financial dashboard.
• Scenario 3: Investment Appraisal and Valuation Use AI to assist with building a DCF model, reviewing NPV and IRR formulas, identifying important assumptions, and comparing investment scenarios. Validate all outputs before recommending an investment decision.
• Expected Practical Outputs Participants will work towards producing an AI-assisted financial analysis workbook, financial ratio analysis, budget variance report, financial forecast, integrated financial model, cash flow projection, investment appraisal model, DCF valuation, scenario and sensitivity analysis, financial risk assessment, SPSS or Stata analysis report, Power BI dashboard, executive financial briefing, AI output verification checklist, and a responsible AI usage framework for financial analysis.

Frequently asked questions

Schedule & fees