The Project Finance Modelling Training Course provides a comprehensive and practical framework for building, analyzing, and applying financial models to capital-intensive projects, infrastructure investments, energy developments, and other long-term projects. The course is designed to help finance, investment, project development, banking, and corporate professionals translate operational, commercial, and financing assumptions into integrated financial models that support project feasibility assessment, funding decisions, debt capacity analysis, investment evaluation, and risk management.
The program focuses on developing a complete project finance model that begins with key project assumptions and progresses through revenue forecasting, operating costs, capital expenditure, working capital, cash flow projections, financing structure, debt schedules, debt service, and investment returns. Participants will learn how to establish clear relationships between project operations, financing requirements, cash generation, and debt repayment capacity.
The course covers the core principles of project finance, including debt and equity structures, leverage, financing costs, interest calculations, repayment mechanisms, debt service coverage, financial covenants, and funding requirements. Particular attention is given to understanding how changes in project revenues, operating costs, capital expenditure, interest rates, construction delays, financing terms, and operating assumptions can affect project viability and financial performance.
Through practical case studies, modelling exercises, financial workshops, scenario analysis, sensitivity testing, and stress testing, participants will develop the ability to build, review, challenge, and present project finance models. The course supports government entities, banks, financial institutions, oil and gas organizations, energy companies, infrastructure developers, investors, and large corporations in strengthening project evaluation, financing decisions, capital planning, and financial risk assessment.