Technical Guides

AI Adoption Framework

AI adoption in a finance function is most reliable when treated as a staged progression rather than an immediate wholesale rollout: exploratory pilots on low-stakes tasks, supervised production use on defined tasks with human checkpoints, and a fully governed operating model with defined ownership and controls. This guide sets out each stage, the specific conditions an organisation should meet before advancing, and why skipping stages tends to produce ungoverned, inconsistent adoption rather than faster value capture.

AI Assurance Framework

Ongoing assurance over AI-assisted finance work depends on connecting three activities that are often run separately, quality assurance sampling, internal control testing, and periodic model re-validation, into a single assurance cycle with a shared reporting line. This guide sets out how these three activities complement each other, why running them in isolation leaves gaps each is well positioned to catch for the others, and how to structure a combined assurance cycle.

AI Audit Trail

An audit trail for AI-assisted financial work should capture more than the final output: the prompt or task input, the specific model or technique version used, the source material supplied, the verification checkpoint outcome, and the human decision applied to the result. This guide sets out what a complete AI audit trail captures and why each element matters specifically for defending an AI-assisted conclusion after the fact, to an auditor, regulator, or internal governance review.

AI Budgeting Models

AI-assisted budgeting uses machine learning to suggest a driver-based baseline from historical actuals and generative AI to draft first-pass budget narrative, accelerating the mechanical portion of the budgeting cycle. This guide sets out where each technique adds value, why the approved budget figure should remain a product of budget-holder judgement rather than an AI-generated number, and the review practices that keep an AI-assisted budget defensible.

AI Cash Flow Forecasting

AI cash flow forecasting applies machine learning to predict payment timing and collections risk across a large number of customer or vendor accounts, a task well suited to pattern learning at scale. This guide sets out where machine learning adds value in cash flow forecasting specifically, distinct from revenue or expense forecasting, and why working capital policy assumptions, payment terms, discount policy, remain a treasury judgement input rather than a model-derived one.

AI Centre of Excellence

A finance function's AI centre of excellence should function as an operating capability, not a nominal committee: maintaining technique-task matching guidance as new applications emerge, running the independent quality assurance sampling programme, and owning the AI risk register on behalf of the organisation. This guide sets out these responsibilities concretely and the signs that distinguish a functioning centre of excellence from a name on an org chart with no operational activity behind it.

AI Decision Support

AI decision support brings together the forecasting, scenario, sensitivity, valuation, and portfolio analytics applications addressed across this domain into a single question: how should AI-generated analysis actually inform a finance decision. This guide sets out a decision framework that keeps AI output positioned as an input, presented alongside its confidence basis and limitations, with the decision itself remaining a human accountability that cannot be delegated to a tool regardless of how sophisticated its analysis appears.

AI Ethics in Finance

AI ethics in finance addresses considerations distinct from, though related to, the regulatory and governance topics covered elsewhere in this domain: fairness in decisions an AI-generated analysis influences, appropriate transparency with parties affected by an AI-influenced decision, and accountability that remains with a human or institution regardless of how sophisticated the underlying AI analysis was. This guide sets out these considerations as practical questions a finance function should be able to answer about its own AI use.

AI Explainability

Explainability, the ability to state why an AI model produced a specific output, means something different for machine learning than for generative AI, and something different again from the formula traceability standard applied to a deterministic financial model. This guide sets out each of these distinct explainability standards, why conflating them creates unrealistic expectations for what an AI model can actually explain about itself, and the practical documentation, feature importance, training data description, known limitations, that supports explainability in finance practice.

AI Finance KPIs

Measuring whether AI adoption in a finance function is actually working requires a small set of specific KPIs read together, output accuracy against a verified benchmark, checkpoint pass rate, time saved net of verification effort, and adoption maturity by task. This guide defines each KPI, how it should be measured, and why no single KPI in isolation is sufficient to judge whether a given AI application is delivering genuine value.

AI Financial Controls

AI-specific verification checkpoints, source verification, number tie-outs, formula review, should be integrated into a finance function's existing internal controls framework as testable controls, not treated as a separate, informal practice sitting outside standard control testing. This guide sets out how to document an AI-assisted process's checkpoints as formal controls, how they should be tested, and why integrating them into existing controls testing produces stronger assurance than a parallel, AI-specific control process.

AI Financial Model Validation

Validating an AI-assisted financial model requires separating two genuinely different tasks: validating the model's own structured, auditable calculation logic, using the same methodology applied to any financial model, and validating any embedded AI-derived assumption or prediction, using empirical accuracy measurement against held-out data. This guide sets out both validation tracks and why conflating them produces an incomplete validation of either.

AI Financial Modelling Best Practices

This capstone guide synthesises the technique-matching discipline, the AI-informed decision framework, and the governance structure established across this domain into a single set of best practices for applying AI to financial modelling and finance functions reliably. It is intended as the reference point a finance function can return to when evaluating any new AI application against the discipline this domain has built up wave by wave, rather than a new set of principles introduced for the first time here.

AI Forecasting Models

AI forecasting models use machine learning to predict a future value from patterns learned in historical data, complementing traditional driver-based forecasting rather than replacing it. This guide sets out the training data requirements a machine learning forecast depends on, how its accuracy should be measured and monitored over time, and the specific forecasting tasks where a machine learning approach adds genuine value over a traditional driver-based model.

AI Governance Framework

A complete AI governance framework connects the individual governance components addressed across this domain, model governance, financial controls, documentation, assurance, periodic audit, ethics, and regulatory considerations, into a single institutional structure with defined ownership at each level. This guide sets out that complete structure, how its components relate to one another, and the governance framework as the top-level synthesis of every governance and risk practice this domain has established.

AI Hallucination Risk

Hallucination, a generative AI model producing plausible-sounding but fabricated content, is the single most consequential risk in applying generative AI to finance. This guide explains why hallucination occurs as a structural property of how language models generate text, the specific finance contexts where it carries the most consequence, citations, figures, and factual claims feeding a material decision, and the layered controls, source grounding, verification checkpoints, and ongoing output monitoring, that manage the risk in practice.

AI Investment Analysis

AI investment analysis applies natural language processing to accelerate due diligence document review, machine learning to support deal screening against defined criteria, and generative AI to draft first-pass investment memo narrative. This guide sets out where each application adds genuine value within an investment process, and why the investment thesis, risk assessment, and final recommendation remain the investment professional's judgement, informed by AI-accelerated research rather than produced by it.

AI Model Audit

An AI model audit is the independent, periodic examination of an AI-assisted financial model over its operational life, distinct from the one-time validation performed at deployment. This guide sets out what a periodic AI model audit covers, documentation currency against the model's actual current state, evidence that the process controls have genuinely operated since the last audit, and accumulated drift since the last re-validation, and how it complements rather than duplicates the deterministic and generative audit methodology addressed on AI Financial Model Audit.

AI Model Documentation

Documenting an AI model used in finance requires elements beyond standard financial model documentation: a description of the training data or source material used, the specific technique category applied, known limitations, and validation history over time. This guide sets out each element, why each supports a specific downstream use, governance review, audit, onboarding a new team member, and how this documentation connects to the governance and audit trail practices addressed elsewhere in this domain.

AI Model Governance

AI model governance establishes ownership, documented scope and limitations, change control, and periodic re-validation for machine learning and generative AI models used within a finance function. This guide sets out the governance elements specific to AI models, distinct from but complementary to the financial model governance a firm already applies to its spreadsheet and system models, and why an AI model's statistical nature requires governance triggers a static formula-based model does not.