Technical Guides

AI Portfolio Analytics

AI portfolio analytics applies machine learning across a portfolio of investments or assets to detect risk clustering, correlation patterns, and early warning signals that would be difficult to identify manually at portfolio scale. This guide sets out where machine learning adds genuine value in portfolio-level analysis, and why portfolio strategy, capital allocation decisions, and the interpretation of a flagged signal remain a governance and judgement responsibility rather than an automated output.

AI Quality Assurance

A quality assurance programme for AI-assisted finance work applies periodic, sampling-based review of AI-assisted output independent of the task-level verification checkpoints, closing the gap those checkpoints alone can leave. This guide sets out how to structure a QA sampling programme, how it connects to the KPI set already used to measure AI adoption, and how QA findings should feed back into governance, checkpoint design, and adoption stage decisions.

AI Regulatory Considerations

AI use in finance intersects with a developing regulatory landscape, general AI risk management frameworks, sector-specific financial regulation, and jurisdiction-specific requirements that vary materially by location and use case. This guide sets out the categories of regulatory consideration relevant to AI use in finance at a general level, framed explicitly as considerations to raise with qualified legal counsel rather than as legal advice, since specific regulatory obligations depend on jurisdiction, sector, and the specific AI application involved.

AI Risk Management

AI risk management brings together the distinct risk categories addressed across this domain, hallucination, model drift, explainability limitations, fairness, regulatory exposure, and accountability diffusion, into a single risk register structure a finance function can maintain and review as part of its broader risk management practice. This guide sets out that register structure and how it connects to the governance, validation, and quality assurance practices addressed elsewhere in this domain.

AI Scenario Planning

AI-assisted scenario planning uses generative AI to draft a wider range of plausible scenario variations around a base case than a modeller might generate manually, accelerating the ideation stage of scenario construction. This guide sets out that role, why an AI-drafted scenario must still be checked for internal consistency before use, and why probability weighting across scenarios remains a judgement exercise no AI tool performs on its own.

AI Sensitivity Analysis

AI-assisted sensitivity analysis uses machine learning to help rank which drivers most influence a model's output across historical data, directing attention to the variables worth testing most rigorously, and generative AI to draft commentary explaining sensitivity results. This guide sets out that role and why the specific sensitivity ranges tested against each driver should remain a modeller's defined, documented, and auditable input rather than an AI-generated range.

AI Transformation Roadmap

An AI transformation roadmap for a finance function sequences three parallel tracks, building technique and adoption foundations, expanding enterprise applications, and maturing governance and risk practice, rather than treating governance as a final phase to address only after applications have scaled. This guide sets out that sequencing, why the three tracks should progress together rather than strictly in series, and the milestones that mark genuine progress on each.

AI Valuation Support

AI valuation support uses natural language processing and machine learning to accelerate comparable company screening and market data research, and generative AI to draft first-pass valuation narrative, within a standard discounted cash flow or comparable company valuation process. This guide sets out where these applications add genuine value and why the valuation methodology, discount rate determination, and final judgement on value remain the analyst's responsibility, informed by rather than delegated to AI.

AI for FP&A

FP&A is one of the finance functions where AI adoption has progressed furthest, spanning budgeting, forecasting, variance analysis, and management reporting. This guide maps AI application across the FP&A cycle, distinguishes tasks where machine learning-driven prediction is well matched from tasks better suited to generative AI drafting, and sets the frame for the more specific budgeting, forecasting, and analysis guides that follow it in this domain.

AI for Financial Analysts

Financial analysts increasingly use AI tools as part of daily workflow, synthesising research, explaining variances, drafting first-pass commentary, and assisting with formula construction. This guide sets out where these tools reliably save analyst time, and the verification habits, source checking, number tie-outs, formula review, that keep AI-assisted analyst work at the same reliability standard as unassisted work.

AI in Financial Modelling

AI in financial modelling refers to the application of machine learning and generative AI techniques within the modelling process itself, rather than across the finance function broadly: identifying candidate drivers from historical data, assisting with formula and structure construction, generating scenario variations, and drafting narrative commentary around a model's output. This guide sets out where these applications add genuine value and, just as importantly, where the calculated number itself must remain the output of a structured, auditable model rather than of the AI directly.

AI-Assisted Financial Analysis

AI-assisted financial analysis works best when structured as a defined workflow rather than an ad hoc use of a chat tool: decomposing an analysis into discrete tasks, assigning each task to the approach best suited to it (AI-assisted or human-led), and placing a human verification checkpoint at each point where AI output feeds into a conclusion. This guide sets out that workflow structure and the checkpoint discipline that keeps it reliable.

Acute Care Hospital Models

Acute care hospitals treat the most clinically complex and time-sensitive patient population in the healthcare system, with revenue and cost structures shaped by emergency department throughput, emergency-to-inpatient admission conversion, and a generally higher case mix index than other provider types. This guide applies the general hospital financial model architecture to the acute care setting specifically: how emergency department volume and conversion rate feed the inpatient forecast, and why acute care cost structure runs materially higher than lower-acuity settings.

Adjusted Present Value (APV) Method

The Adjusted Present Value (APV) method values a business in two separate steps rather than blending financing effects into a single discount rate: first, the value of the firm as if it were entirely equity-financed, discounted at the unlevered cost of capital; second, the present value of financing side effects — primarily the interest tax shield — discounted separately. This guide sets out why that separation matters, the mechanics of the two-step build, the discount rate convention used for the tax shield, when APV is preferred over WACC-based DCF (chiefly where the debt schedule is known and changing, as in a leveraged buyout), a worked numeric illustration, and the structural audit checks that confirm an APV build has been implemented correctly.

Affordable Housing Model Structure

Affordable housing spans development (integrated within a mixed-tenure scheme or built standalone) and, once complete, long-term rent-capped income, and requires its own tenure-level segmentation, a grant and subsidy funding stack layered alongside conventional debt and equity, and a rent-capped income model distinct from market-rate residential. This guide sets out how each of these mechanics should be represented, extending the general treatment introduced in Residential Development Model Structure.

Airport Operations Financial Models

An airport operations financial model represents the asset's ongoing revenue, cost, and asset lifecycle structure once in service: aeronautical revenue (landing fees, passenger charges) alongside non-aeronautical revenue (retail, parking, property), runway and terminal asset renewal on their own distinct lifecycles, and, where applicable, a regulatory tariff reset mechanism. This guide covers how to build that operations-phase model from the asset owner or operator's ongoing asset management perspective, complementing the transaction-focused audit treatment in Financial Model Audit for Airports.

Ambulatory Surgery Centre Models

Ambulatory surgery centres perform same-day surgical procedures outside the hospital inpatient setting, with financial performance driven by operating room utilisation, case turnover time between procedures, and procedure-mix profitability rather than bed occupancy. This guide covers how to model operating room capacity and scheduling, why case turnover time is a direct throughput and revenue driver, and how procedure mix should be modelled at the individual case-type level.

Artificial Intelligence in Finance

Artificial intelligence in finance spans a wide range of techniques, machine learning, natural language processing, and generative AI, applied across a wide range of finance functions, financial modelling, FP&A, risk management, treasury, and audit. This guide sets out the main categories of AI technique in practical finance use today, the finance functions each is best suited to, and the foundational distinction between AI applied to raw data (prediction, classification) and AI applied to language and reasoning (generation, summarisation), as the entry point for the more specific guides in this domain.

Asset Acquisition Models

An asset acquisition model differs structurally from a share acquisition model in three specific ways — the buyer's tax basis in the acquired assets is typically stepped up to purchase price, only the specifically itemized assets and liabilities transfer (rather than the entire legal entity), and material contracts typically require individual re-assignment or counterparty consent rather than transferring automatically. This guide covers how each of these differences should be structured in the model, building on the standalone-projection and purchase price allocation mechanics already covered on Merger Model and Accretion/Dilution Structure.

Asset Lifecycle Financial Models

An asset lifecycle financial model represents an infrastructure asset's full economic life — planning and design, construction or acquisition, the operating phase, one or more renewal or major refurbishment cycles, and eventual disposal or decommissioning — as a single connected structure, rather than treating each phase as an independent model. This guide covers how to architect a lifecycle model: the phase transitions that must be explicitly modelled, how renewal cycles recur across the asset's life, and why a model scoped to a single phase systematically understates total cost of ownership.