Certificate in Financial Modelling: A Comprehensive Journey into the World of Financial Modeling

Introduction

Financial models sit behind almost every significant decision an organisation makes: whether to sanction a new field development, extend the life of a power plant, refinance debt, or approve next year’s budget. Yet many models in daily use are fragile, undocumented and dependent on arbitrary assumptions such as a flat growth rate. This training course addresses that gap by combining sound accounting and finance theory with rigorous, transparent spreadsheet design and evidence-based statistical forecasting.

Energy organisations face additional modelling challenges: volatile commodity prices, long and capital-intensive project lives, fiscal regimes and production profiles in Oil & Gas, and long construction periods, high fixed costs, capacity factors and decommissioning obligations in nuclear and power generation. The course equips participants to capture these realities in their models while keeping them flexible, auditable and easy to present to decision makers.

This GLOMACS Certificate in Financial Modelling training course will highlight:

• Applications of Excel from core functions to advanced analytical and automation tools
• Building dynamic, integrated income statement, balance sheet and cash flow models
• Business statistics, regression, correlation and ANOVA for evidence-based forecasting and target setting
• Investment appraisal (NPV, IRR, MIRR, XNPV, XIRR, payback) and discount rate estimation
• Sensitivity, scenario, stress testing and Monte Carlo simulation for risk-aware decisions
• Sector applications for Oil & Gas (upstream and downstream) and Nuclear / Power generation
• Responsible use of AI tools to build, audit, document and explain financial models

Objectives

By the end of this Certificate in Financial Modelling training course, participants will be able to:

• Apply financial accounting fundamentals and the IFRS Conceptual Framework to structure reliable financial models
• Design models following recognised best practice: separated inputs, calculations and outputs, consistent formulas and built-in error checks
• Use descriptive statistics, trend and time series analysis, regression and ANOVA to set realistic forecasts and targets
• Build an integrated three-statement model with depreciation, working capital and debt schedules
• Estimate an appropriate discount rate (cost of equity via regression and WACC) for corporate and energy projects
• Evaluate investments using NPV, IRR, MIRR, XNPV, XIRR, payback and, for power projects, the Levelised Cost of Electricity (LCOE)
• Quantify risk through sensitivity tables, scenarios, stress tests, break-even analysis and Monte Carlo simulation
• Analyse financial performance using ratio pyramids and predict financial distress
• Use AI assistants productively and safely to accelerate model building, auditing and reporting

Training Methodology

This training course uses a blend of instructor-led training, hands-on Excel modelling exercises, interactive discussions, and real-world case studies to facilitate active learning and retention.

Organisational Impact

By allocating personnel to this training course, the organisation will benefit through:

• Improved quality and consistency of investment and budgeting decisions
• Transparent, auditable models that reduce the risk of spreadsheet errors
• Forecasts and targets grounded in statistical evidence rather than arbitrary assumptions
• Better quantification of commodity price, cost and schedule risks in energy projects
• Faster analysis and reporting through Excel automation and responsible AI adoption
• Stronger financial competencies supporting strategic planning and performance monitoring

Personal Impact

As a direct result of attending this training course, participants will:

• Gain advanced, practical ability in financial modelling using Excel
• Build integrated models independently and with confidence
• Be able to challenge and validate models prepared by others
• Use statistics to measure performance and set consistent targets
• Work productively with AI tools while understanding their limitations
• Contribute more effectively to corporate and project decisions