Project Finance Modelling

Project Finance Modelling

Course schedule

Classroom Training:
DateVenueDurationPrice
8 - 12 Jun 2026London5 days£4,495
17 - 21 Aug 2026London5 days£4,495
2 - 6 Nov 2026London5 days£4,495

Please note: prices shown above are exclusive of VAT (20%).

If you don’t see your preferred course date, please contact us.

Course Overview

This course focuses on quantitative methods for assessing financial and operational risk. Delegates study probability analysis, stress testing, and Monte Carlo simulation. Participants learn to model uncertainty and assess potential outcomes. The programme equips professionals to integrate risk models into strategic planning and decision-making.

Who Should Attend

Professionals managing financial structuring and analysis for major capital projects. Past delegates have included:

  • Project Finance Managers
  • Investment Analysts
  • Infrastructure Specialists
  • Financial Controllers
  • Corporate Finance Officers

Course Outcomes

  • Build robust project finance models covering debt, equity and cash flow structures.
  • Evaluate bankability through sensitivity and ratio analysis.
  • Incorporate construction, operation and risk parameters into models.
  • Prepare model documentation for investors and lenders.
  • Test project feasibility under different funding and market conditions.

Course Topics

Structuring Project SPV Models

  • Model phased construction and operations with clear timelines.
  • Build revenue, O&M, reserve, and covenant schedules.
  • Incorporate tax, depreciation, and funding constraints accurately.
  • Create base, lender, and equity cases for decision review.

Debt, Equity, and Covenant Mechanics

  • Size and sculpt debt with refinancing and cash sweep features.
  • Calculate DSCR, LLCR, and project IRR under multiple scenarios.
  • Model lock‑ups, distribution tests, and covenants robustly.
  • Evidence lender compliance with transparent audit checks.

Excel Best Practices for Financial Modelling

  • Structure models with separate inputs, calculations, and outputs to improve auditability.
  • Use consistent naming conventions, dynamic ranges, and version control to reduce errors.
  • Implement checksums and reconciliation flags to validate calculations end‑to‑end.
  • Document assumptions, sources, and logic to support review, handover, and maintenance.

Ensuring Data Integrity and Model Accuracy

  • Design robust data import and transformation steps with traceable lineage.
  • Apply validation tests (type, range, and logic checks) to catch anomalies early.
  • Minimise hard‑coding; build driver‑based links that prevent circularity and drift.
  • Create error‑handling and exception logs that surface issues for timely fixes.

Communicating Financial Insights Effectively

  • Translate model outputs into clear narratives that answer the ‘so what?’.
  • Use charts, bridges, and scenarios to highlight drivers, risks, and options.
  • Present concise recommendations with quantified impacts and sensitivities.
  • Package model files and executive packs for efficient decision meetings.

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