AI-Assisted Modelling & Analysis Checklist
Executive Summary
Key Takeaways
- ✓ AI-assisted modelling and analysis introduces verification requirements beyond a standard model review, specific to how the AI technique was matched to the task and how its output was checked.
- ✓ Confirming the AI technique used, machine learning, NLP, or generative AI, was actually suited to the specific task is the first and most consequential check on this checklist.
- ✓ Every AI-drafted formula, figure, and citation should show evidence of independent verification before the work is relied upon, not merely an assertion that verification occurred.
- ✓ AI-generated analysis should be presented to any decision-maker alongside its confidence basis and limitations, confirming it is functioning as a decision input rather than the decision itself.
Objective¶
This checklist verifies AI-assisted financial modelling and analysis work before it supports a decision, specific to the technique-matching, verification, and decision-framing practices set out across AI Financial Modelling & Artificial Intelligence in Finance. It supplements, and does not replace, the structural formula testing baseline addressed on Financial Model Auditing.
Applicability¶
Applicable whenever AI, machine learning, natural language processing, or generative AI, has been used to assist in constructing a financial model, drafting analysis, or producing forecasting, scenario, sensitivity, valuation, or portfolio output that will inform a material decision.
Checklist¶
| # | Check Item | Why It Matters | Evidence to Collect |
|---|---|---|---|
| 1 | The AI technique used (machine learning, NLP, or generative AI) is matched to the specific task it was applied to | Applying the wrong category of technique to a task undermines reliability regardless of any downstream review | Documented technique and task pairing |
| 2 | Every AI-drafted formula has been independently reviewed for calculation logic | A drafted formula can be syntactically valid while applying incorrect logic | Reviewer sign-off on formula logic |
| 3 | Every AI-generated figure, fact, or citation used in material supporting a decision has been independently verified against its original source | Generative AI can produce plausible-sounding but fabricated content | Source verification record for each material claim |
| 4 | Machine learning-suggested drivers, baselines, or predictions have been reviewed as candidates for judgement, not accepted as finished assumptions | Historical patterns do not by themselves establish forward-looking relevance | Documented judgement review of each AI-suggested input |
| 5 | AI-drafted narrative or commentary has been tied back to the actual underlying numbers before finalisation | Fluent narrative can misstate a variance, driver, or conclusion while reading plausibly | Number tie-out record |
| 6 | Verification checkpoints are placed at each point AI-assisted output feeds into a downstream conclusion, not only at the end of the process | An error introduced early can propagate through several downstream steps before a final review would catch it | Checkpoint log across the workflow |
| 7 | AI-generated analysis presented to a decision-maker includes its confidence basis and known limitations | Presenting AI output as an unqualified conclusion removes the context a decision-maker needs to weigh it appropriately | Decision memo or presentation showing basis and limitations stated |
| 8 | The final decision was made by a human weighing AI-generated analysis against other inputs, not triggered automatically by the AI output | Accountability for a material decision cannot be delegated to a tool | Decision record showing human judgement applied |
| 9 | Output accuracy, checkpoint pass rate, and adoption maturity for the relevant task have been tracked per the organisation's AI Finance KPIs | Aggregate or absent measurement obscures which specific AI applications are delivering genuine value | KPI tracking record for the task |
Common Failures¶
- Generative AI applied to a numerical prediction task better suited to machine learning, or vice versa, without documented rationale for the technique choice.
- AI-drafted formulas or figures relied upon without a documented independent verification step.
- Machine learning driver suggestions carried through into a budget or forecast without judgement review.
- AI-generated analysis presented to a committee or board without its confidence basis or limitations stated.
- A decision treated as automatically determined by an AI output, with no documented human weighing step.
Recommended Evidence¶
A completed AI-assisted modelling and analysis review should be accompanied by a documented technique-task pairing rationale, verification records for material AI-drafted formulas, figures, and citations, and a decision record showing the human judgement applied alongside the AI-generated analysis. The table above is structured for direct use in a model governance file, an internal review working paper, or an audit evidence file supporting a material decision.
How to Use This Checklist¶
Apply the general Financial Model Auditing structural checks first, then work through this checklist against the specific AI-assisted elements of the model or analysis. See AI-Assisted Financial Analysis and AI Decision Support for the full workflow and decision-framing practice this checklist verifies.
Continue Reading¶
Related Pillars¶
Related Technical Guides¶
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Frequently Asked Questions
What makes AI-assisted modelling and analysis different from standard model review, for checklist purposes?
It introduces verification requirements specific to how AI was used, whether the technique was matched to the task, whether AI-drafted formulas and figures were independently checked, and whether AI output is being treated as an input to a decision rather than the decision itself, none of which a generic model review checklist addresses.
Why is technique-task matching the first item on this checklist?
Because using generative AI for a numerical prediction task, or machine learning for a drafting task, misapplies the technique before any further review can meaningfully help, making this the most consequential check to confirm early.
What evidence should be collected for AI-drafted formula or figure verification?
A record that the specific formula, figure, or citation was independently checked, against source data, a reviewer's own calculation, or an original document, not merely a statement that review occurred without supporting evidence.
How does this checklist confirm AI output is being used appropriately in a decision?
By verifying that any AI-generated analysis presented to a decision-maker includes its confidence basis and known limitations, and that the decision itself was made by a human weighing that analysis against other inputs, not automatically triggered by the AI output.
Who typically uses this checklist?
Financial modellers and FP&A teams producing AI-assisted work, and reviewers or auditors checking that work before it is relied upon to support a material decision.
How does this checklist relate to the general financial model audit checklist?
It supplements the general financial model audit baseline addressed on Financial Model Auditing with checks specific to AI-assisted construction and analysis; it does not replace the underlying structural formula testing a standard model audit performs.
Related Articles
AI Financial Modelling & Artificial Intelligence in Finance
AI financial modelling is the application of machine learning and generative AI techniques within the financial modelling process itself, driver identification, construction assistance, scenario generation, and narrative drafting, while artificial intelligence in finance is the broader application of those same technique categories across the finance function generally. This page is the hub for the Knowledge Centre's AI financial modelling content: the foundational distinction between machine learning, natural language processing, and generative AI; how AI accelerates modelling construction without replacing the auditable calculation layer beneath it; a staged framework for adopting AI reliably; enterprise applications across FP&A, forecasting, valuation, and investment analysis; governance and risk practice; and the institutional best practice synthesis this domain builds toward.
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.
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.
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.
What Is a Financial Model Audit?
A financial model audit is an independent, structured examination of an Excel based financial model to confirm that its mechanics, logic, and outputs are reliable enough to support a decision. It is not a check of whether the assumptions are optimistic or conservative. It is a check of whether the model actually calculates what its author believes it calculates. Every year, lenders extend debt, investment committees approve capital, and boards sign off on transactions using numbers that came out of a spreadsheet nobody outside the immediate deal team has independently verified. A financial model audit exists to close that gap before it becomes expensive.