Advanced Financial Modelling, Quantitative Finance and Econometrics · Physical
Sensitivity Analysis and Financial Scenario Modelling Using Excel, Power BI and Python
10 Days1 sessions available Certificate on completion
The Sensitivity Analysis and Financial Scenario Modelling Training Workshop equips finance professionals, financial analysts, accountants, investment managers, economists, project managers, and business leaders with practical techniques for assessing financial uncertainty, evaluating alternative assumptions, and making informed strategic decisions. The course focuses on understanding how changes in key financial variables affect profitability, cash flow, investment returns, project viability, and overall business performance.
Participants learn to develop financial models, identify key value drivers, perform one-variable and multi-variable sensitivity analysis, construct best-case, base-case, and worst-case scenarios, and evaluate the impact of changes in revenue, operating costs, interest rates, inflation, exchange rates, taxation, and capital expenditure. The workshop covers spreadsheet-based modelling, scenario planning, break-even analysis, stress testing, Monte Carlo simulation, and risk-adjusted investment appraisal using practical financial datasets and business case studies.
Course objectives
1. Develop robust financial models using Microsoft Excel to analyse revenue, operating costs, profitability, cash flows, budgets and investment performance.
5. Develop interactive financial dashboards using Power BI to compare scenarios, monitor financial performance, communicate risk exposure and support evidence-based budgeting, investment and strategic management decisions.
4. Use Python for advanced financial analysis by applying Pandas, NumPy and SciPy to automate calculations, perform multi-variable sensitivity analysis and conduct Monte Carlo simulations of uncertain financial outcomes.
3. Apply financial forecasting and risk assessment techniques to perform stress testing, evaluate investment viability, identify financial vulnerabilities and assess alternative business scenarios.
2. Conduct sensitivity and scenario analysis to evaluate how changes in key financial assumptions, including inflation, interest rates, exchange rates and sales volumes, affect financial outcomes.
Who should attend
Financial analysts, accountants, finance managers, investment analysts, corporate finance professionals, treasury officers, risk managers, budget officers, project finance specialists, economists, business consultants, project managers, business owners, financial planners, and professionals involved in investment appraisal, budgeting, forecasting, and strategic decision-making.
Course content
Module 1: Fundamentals of Financial Modelling, Sensitivity Analysis and Scenario Planning Financial modelling principles, role of sensitivity analysis in financial decision-making, difference between sensitivity analysis and scenario analysis, financial
Module 2: Financial Model Design, Assumptions and Key Performance Drivers Financial model structure and best practices, identification of key financial assumptions, revenue and cost drivers, model inputs and outputs, formula-based calculations, separatio
Module 3: One-Variable and Multi-Variable Sensitivity Analysis Using Excel Single-variable sensitivity analysis, two-variable sensitivity analysis, identification of key financial drivers, Excel Data Tables, Goal Seek, Scenario Manager, sensitivity of re
Module 4: Advanced Excel What-If Analysis and Financial Modelling Excel What-If Analysis tools, Scenario Manager for comparing financial assumptions, Goal Seek for target-based financial calculations, Data Tables for analysing changing assumptions, use o
Module 5: Revenue, Cost, Profitability and Break-Even Modelling Revenue forecasting models, fixed and variable cost structures, contribution margin analysis, gross and operating profit modelling, break-even point calculations, price-volume-cost relations
Module 6: Cash-Flow Forecasting, Working Capital and Liquidity Scenarios Cash-flow forecasting principles, operating and investing cash flows, working capital assumptions, accounts receivable and payable scenarios, inventory holding and cash conversion c
Module 7: Investment Appraisal and Capital Budgeting Sensitivity Analysis Capital budgeting principles, capital expenditure forecasting, Net Present Value (NPV), Internal Rate of Return (IRR), payback period, discounted cash-flow modelling, sensitivity o
Module 8: Budgeting, Financial Forecasting and Variance Analysis Budget development and financial forecasting, operating and capital budgets, budget assumptions and financial drivers, actual-versus-budget analysis, favourable and unfavourable variances,
Module 9: Interest Rate, Inflation and Foreign Exchange Sensitivity Interest rate movements and borrowing costs, inflation assumptions and operating expenditure, foreign exchange exposure, exchange rate effects on imports and exports, foreign currency lo
Module 10: Best-Case, Base-Case and Worst-Case Financial Scenario Modelling Development of base-case assumptions, optimistic and pessimistic financial scenarios, revenue growth and contraction scenarios, operating cost escalation, cash-flow and profitabi
Module 11: Financial Stress Testing and Risk Exposure Assessment Financial stress-testing principles, identification of material financial risks, adverse revenue and expenditure shocks, interest rate and currency stress tests, liquidity stress testing, d
Module 12: Monte Carlo Simulation and Probability-Based Financial Modelling Using Python Introduction to probability-based financial modelling, uncertainty and probability distributions, normal and uniform distributions, modelling uncertain revenue and c
Module 13: Advanced Multi-Variable Sensitivity Analysis and Financial Risk Modelling Relationships between financial variables, simultaneous changes in revenue, costs, interest rates and exchange rates, correlation and dependency assumptions, multi-varia
Module 14: Financial Scenario Visualisation and Decision-Support Dashboards Using Power BI Financial data preparation and modelling, importing Excel and Python outputs into Power BI, financial performance measures using DAX, actual-versus-forecast report
Module 15: Integrated Financial Sensitivity Analysis and Scenario Modelling Project Development of an integrated financial model using Excel, definition of financial assumptions and performance drivers, preparation of base-case and alternative scenarios,
General notes
Tools and Software Microsoft Excel for financial modelling, financial formulas, sensitivity analysis, What-If Analysis, Goal Seek, Scenario Manager, Data Tables, budgeting, cash-flow forecasting, investment appraisal and financial stress testing, Microsoft Power BI for interactive financial dashboards, scenario comparisons, financial KPIs, budget-versus-actual analysis, risk reporting and management decision support, Python for advanced financial calculations, multi-variable sensitivity analysis, automated scenario generation and Monte Carlo simulation, Pandas for financial data preparation and manipulation, NumPy for numerical calculations and random sampling, SciPy for probability distributions and statistical analysis, Matplotlib and Seaborn for financial charts, simulation outputs and risk visualisation, Excel and CSV files for financial data exchange between tools.
Practical Exercises and Outputs Develop an Excel-based financial sensitivity analysis model to evaluate how changes in revenue, operating costs, interest rates and sales volumes affect profitability, prepare best-case, base-case and worst-case financial scenarios for a business or investment project, build a cash-flow forecasting workbook incorporating working capital assumptions and delayed customer payments, conduct investment appraisal using Net Present Value (NPV), Internal Rate of Return (IRR) and payback period under different financial assumptions, perform budget-versus-actual analysis and identify significant financial variances, develop a financial stress-testing model to assess the impact of inflation, currency depreciation and increased borrowing costs, use Python, Pandas and NumPy to simulate thousands of possible financial outcomes through Monte Carlo simulation, calculate the probability of achieving profit and cash-flow targets using simulated results, rank the most influential financial assumptions through multi-variable sensitivity analysis, create a Power BI dashboard to compare financial scenarios and monitor profitability, liquidity, expenditure and financial risk indicators, and produce an integrated financial scenario analysis report with evidence-based recommendations for management decision-making.
Through hands-on exercises using Microsoft Excel, participants develop dynamic financial models, sensitivity tables, scenario comparison reports, and decision-support tools. Additional applications using Python, Power BI, and statistical analysis tools can be incorporated to automate simulations, visualise financial outcomes, and communicate risks effectively.
By the end of the workshop, participants will be able to assess financial model uncertainty, identify the variables with the greatest influence on business outcomes, test alternative economic and operational conditions, and develop evidence-based recommendations for budgeting, investment appraisal, strategic planning, and financial risk managemen
Frequently asked questions
The workshop is suitable for finance managers, accountants, financial analysts, investment analysts, budget officers, risk managers, business analysts, project managers, financial planners, auditors and professionals involved in budgeting, forecasting, investment appraisal and financial decision-making.
Participants will use Microsoft Excel for financial modelling and sensitivity analysis, Power BI for interactive financial dashboards and scenario comparisons, and Python with libraries such as Pandas, NumPy and SciPy for advanced financial calculations, statistical analysis and Monte Carlo simulation.
No advanced programming experience is required to begin the workshop. Participants can learn the core financial modelling techniques using Excel before progressing to Power BI and guided Python exercises. Basic computer skills and familiarity with spreadsheets are beneficial.
You will learn to build financial models, evaluate best-case and worst-case scenarios, analyse the impact of changing financial assumptions, forecast cash flows, assess investment viability, perform financial stress testing, simulate uncertain financial outcomes and develop dashboards that support evidence-based management decisions.
These techniques help organisations understand how changes in revenue, costs, inflation, interest rates, exchange rates and other assumptions affect financial performance. They support risk identification, budget planning, investment evaluation, contingency planning and the development of more informed financial strategies.