Technical Guides

Financial Due Diligence

Financial due diligence investigates a target company's historical financial performance — earnings quality, working capital trends, net debt, and off-balance-sheet obligations — to establish a reliable, normalized baseline before a transaction is priced. It is distinct from a forward-looking financial model review: financial due diligence establishes what actually happened historically and whether reported earnings are a reliable indicator of sustainable performance, while a model review tests whether the forecast built on top of that baseline is structurally sound. This guide covers financial due diligence's core areas and how its outputs — normalized EBITDA, the net working capital peg, net debt — flow directly into deal pricing.

Financial Model Standards

The two principal standards governing institutional financial model construction are the ICAEW Financial Modelling Code, published by the Institute of Chartered Accountants in England and Wales, and the FAST Standard, published by the FAST Standard Organisation. Both standards address the structure, documentation, and transparency requirements for financial models intended for institutional use, including models submitted for lender review, investment committee approval, and regulatory reporting. The standards differ in their scope and approach: the ICAEW Code provides principles-based guidance applicable to all financial models, while the FAST Standard provides prescriptive rules for model structure applicable to models built under the FAST methodology.

Financing Strategy Considerations

Financing strategy is the process by which a company decides how much debt to raise, on what terms, and when. It draws together several distinct considerations: assessing how much debt the business can safely support given its cash flow and asset base, matching the maturity of new financing to the life of the assets or cash flows it funds, treating covenant headroom as a binding constraint on how aggressively the company can finance itself, and weighing market-timing considerations such as prevailing interest rates and credit market conditions. None of these considerations operates in isolation — a financing decision that looks attractive on debt capacity alone can still be a poor strategic choice if it leaves inadequate covenant headroom or mismatches debt maturity against the cash flows meant to repay it.

Fiscal Regime Modelling

Oil and gas fiscal regimes take one of several forms across jurisdictions, concession and royalty-tax regimes, production sharing contracts, or service contracts, each dividing value between operator and host government through a different mechanism. This guide sets out how to identify which fiscal regime applies to a given asset and jurisdiction, the modelling implications of each type, and why a generic effective tax rate cannot substitute for the actual regime's specific mechanics.

Forecast Methodologies Overview

Before a forecast line is built, a methodology has to be chosen for how it will be projected. This guide compares the four principal forecasting methodologies used across a financial model's revenue and cost lines: top-down forecasting, which starts from a macro or market-level figure and works down; bottom-up forecasting, which builds up from granular unit economics; driver-based forecasting, which structures the model around a defined set of operating drivers regardless of direction; and the percent-of-sales method, which forecasts a line as a constant ratio of revenue. It sets out how the four relate to each other, when each is most defensible, and how they are applied to the revenue and cost sides of a forecast.

Formula Consistency in Financial Models

Formula consistency in a financial model means that cells in the same row or column that perform the same calculation use identical or structurally equivalent formulas. In a time-series financial model, the formula in the Year 1 column of a revenue line should be structurally identical to the formula in the Year 5 column of the same line, with references shifting as appropriate across periods. A cell that contains a formula materially different from its neighbours in the same row is either performing a different calculation intentionally (which should be documented) or contains an error introduced by manual editing.

Formula Error Types in Financial Models

Formula errors in financial models fall into four principal categories: visible error values (including #REF!, #VALUE!, #DIV/0!, #NAME?, #N/A, #NULL!, and #NUM!), which display in cells and are immediately apparent; silent formula errors, which produce plausible-looking values but incorrect results; structural formula errors, which arise from incorrect model construction rather than incorrect values; and logic errors, which occur when a formula correctly implements an incorrect financial relationship. Each category requires different detection methods and carries different risk implications.

Gas Price Scenario Analysis

Natural gas price scenario analysis differs from oil price scenario analysis in one key respect: gas trades at materially different prices across regional hubs, and long-term contracts, particularly in LNG, are frequently priced against a specific indexation formula rather than a single global benchmark. This guide sets out how gas price scenarios should reflect the relevant regional hub or contract indexation basis, and why applying an oil-style single global benchmark misrepresents gas price exposure.

Gas Processing Plant Models

Gas processing plant financial models centre on the extraction of natural gas liquids, ethane, propane, butane and natural gasoline, from raw wellhead gas, and the specific contract structure, fee-for-service, percent-of-proceeds, or keep-whole, under which the plant is compensated. This guide sets out how gas processing economics are modelled around plant recovery rates and the commodity price exposure each contract structure creates for the processor.

Generation Forecast Models

A generation forecast translates a resource yield assessment's confidence-level output figures into the full time-series generation schedule a financial model actually runs on — monthly or hourly granularity, weather-pattern-driven variability, and an explicit uncertainty band around the central forecast. This guide covers how a generation forecast should be built and updated, and why it is a distinct modelling exercise from the resource yield assessment it draws on.

Generative AI in Financial Modelling

Generative AI, large language models applied to drafting and language tasks, has a specific and bounded role in financial modelling: accelerating structure, formatting, and narrative drafting, not producing verified numerical output. This guide sets out that role in detail, the specific failure modes generative AI introduces into a modelling workflow, hallucinated figures, plausible-but-incorrect formula logic, and unverifiable citations, and the concrete review practices that contain each failure mode.

Geothermal Models

A geothermal project carries two risk phases with no direct equivalent in solar or wind: an exploration and drilling phase in which the resource itself is not yet confirmed, and, once operating, a reservoir decline risk in which the geothermal resource's heat and pressure output can decline independent of any equipment degradation. This guide covers how each phase should be modelled, and how reservoir decline should be kept distinct from equipment degradation in the model's technical output build.

Green Finance

Green finance is the use-of-proceeds subset of sustainable finance, capital raised through an instrument, most commonly a green bond or green loan, whose proceeds are contractually restricted to a defined list of eligible environmental projects. This guide covers how eligibility criteria are defined and applied, how proceeds tracking works in practice, and the reporting obligations a green-labelled instrument carries beyond a standard, unrestricted loan or bond.

Green Hydrogen Investment Models

Investing in green hydrogen at portfolio or hub level, across multiple projects sharing common offtake market development risk and policy dependency, requires a different lens than a single project's electrolyzer capacity factor and levelized cost of hydrogen mechanics. This guide covers offtake market development risk aggregated across a portfolio, policy dependency concentration, and blended finance structuring for green hydrogen investment, building on the single-project mechanics covered elsewhere in this Knowledge Centre.

Green Hydrogen Project Models

A green hydrogen project converts renewable electricity into hydrogen through electrolysis, with economics driven by electrolyzer capacity factor (tied to renewable input availability), levelized cost of hydrogen relative to a still-developing offtake market, and, for many current projects, material dependency on production or investment incentives. This guide covers how to model each of these mechanics, distinct from a standard renewable generation project selling electricity directly.

Hardcoded Formulas in Financial Models

A hardcoded value in a financial model is a fixed numeric value embedded directly within a formula cell, rather than being referenced from a dedicated input or assumption cell. Hardcoded values in formula cells are a structural risk because they do not update when the model's assumptions change, they are invisible during normal model navigation, and they cannot be changed consistently through the model's standard input interface. The ICAEW Financial Modelling Code and the FAST Standard both explicitly prohibit hardcoded values within formulas, requiring that all input values be entered in a dedicated input cell and referenced by formulas rather than embedded within them.

Healthcare Business Models

Healthcare providers operate under several fundamentally different business and reimbursement models, fee-for-service, value-based care, capitation, and direct-pay, each of which ties provider revenue to a different underlying mechanism. This guide sets out how each business model's revenue mechanism differs and, correspondingly, how the financial model architecture appropriate to each differs, since applying a fee-for-service-style model to a capitated or value-based business misrepresents the provider's actual revenue and risk exposure.

Healthcare Cost Models

Healthcare operating cost is dominated by staffing, driven by clinical staffing ratios rather than headcount growth, and clinical supply and pharmaceutical costs that scale with case volume and complexity rather than revenue. This guide covers how to build each of these cost categories, why a generic corporate cost growth template understates sector-specific drivers, and how fixed facility overhead should be modelled separately from these variable, activity-driven cost categories.

Healthcare Demand Forecasting

Healthcare demand forecasting operates at the market or catchment level, projecting total addressable clinical demand in a geography and the competitive share a specific provider can expect to capture, distinct from the facility-level operational volume forecasting used to plan day-to-day capacity. This guide covers how to build a catchment area demand model, how competitive market share should be estimated, and how this market-level forecast connects to, without duplicating, facility-level patient volume forecasting.

Healthcare Expansion Feasibility Models

A healthcare expansion feasibility model tests whether a proposed facility expansion or new service line is financially viable, combining market demand validation, a realistic ramp-up curve to maturity, and breakeven analysis against the incremental fixed cost the expansion introduces. This guide covers how to structure each component and why a feasibility model built on mature-state economics alone, without an explicit ramp-up period, systematically overstates near-term returns.