To audit an AI-generated financial model, preserve the exact inputs and assumptions for each run, record the AI-assisted work and human edits, identify the released workbook version, retain test and review evidence, and assign a human owner. A later reviewer should be able to trace material outputs through formulas and assumptions to source data—and repeat the work using the retained records.
What “auditable and reproducible” means for an AI-assisted model
An auditable model lets a reviewer understand what it is for, where its material inputs came from, how calculations produce outputs, what changed, and who checked and approved the released version. A reproducible model preserves enough of the run’s inputs, assumptions, workbook and supporting records for someone else to reconstruct or repeat the analysis.
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AI-generated formulas, explanations and model logic are drafts, not validation. Review depth should reflect the model’s complexity, materiality and intended use. The reviewed authoritative and professional sources do not establish a directly applicable error rate for AI-generated financial models; a generic AI statistic or unrelated spreadsheet-error figure would not answer that question.
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Start with the decision the workbook supports, who will rely on it, which outputs matter, and what an error could affect. Use that assessment to scale documentation, testing, independent review and release controls. An exploratory analysis may need proportionate documentation and a peer check; a model used for reporting, financing, valuation or another consequential decision calls for stronger independent checking, controlled releases and retained evidence. This is a practical risk-based approach, not a universal legal checklist.
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For regulatory context, the US interagency Supervisory Guidance on Model Risk Management, issued by the Federal Reserve, OCC and FDIC on April 17, 2026, supersedes earlier SR 11-7 guidance. It is supervisory guidance for banking organizations, is expected to be most relevant to organizations with more than $30 billion in assets, and says practices should be tailored. It expressly excludes generative and agentic AI; it does not prescribe controls for an AI tool merely because that tool helped build a spreadsheet. The guidance says organizations’ broader governance practices should guide their handling of tools and processes outside its scope. NIST’s AI Risk Management Framework is voluntary, and ICAEW’s spreadsheet principles are professional good practice rather than a statement of law. Applicable obligations still depend on the organization, jurisdiction, model use and internal policies.
What to retain for each AI-assisted model run
Create a model record in an overview sheet or a controlled document attached to the workbook. It should orient a new reviewer without requiring them to infer the model’s purpose or conventions from formulas alone.
Model identity and operating context
- Purpose, intended use, owner, preparer and relevant approval roles.
- Workbook version and run date, units, currency and scale, sign conventions, operating steps and control instructions.
- Key assumptions, limitations, material outputs, source list, external connections and queries.
AI activity and human decisions
- AI tool and model version, if available; date; and the task or prompt specification.
- Relevant input data or a controlled snapshot, generated formulas or code, and the resulting workbook.
- Material human edits, tests performed, reviewer comments, exceptions and their resolution, and final approval.
Keep records in an organization-approved location with access and retention appropriate to the data and intended use. Do not enter confidential financial data into an unapproved AI tool. Whether a particular AI service is permitted depends on the organization’s data, security, retention and vendor policies; there is no universal approved-service list established by the sources cited here.
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How to preserve source and assumption lineage
For every material input, record enough detail to identify what was used and how it entered the model. Include its source, extraction date or version, owner, unit, currency and scale, and any transformation. Reconcile external values and system extracts to their sources. If a live source can change, retain a controlled snapshot or immutable reference for the released run. Note whether linked data refreshes automatically or requires a manual action, and record the refresh status relevant to the run.
Keep inputs, calculations and outputs visibly distinct. Where practical, enter each assumption once in a clearly identified control area rather than copying it into multiple formulas. When assumptions change for a scenario, preserve the scenario inputs and resulting outputs so the comparison remains understandable.
How to version the workbook and explain changes
Use a consistent release naming scheme and retain approved prior versions. A change log should identify the date, version, author and reviewer; describe changes to assumptions, formulas or data and why they were made; and state their effect on important outputs. This explanation is essential: a stored earlier workbook can show what existed, but not by itself why a change occurred or whether it was correct.
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ICAEW’s Financial Modelling Code resource recommends model-run and change-log practices. ICAEW also describes version history in SharePoint/OneDrive and Google Drive as a way to help compare or recover versions. Treat those features as support for a controlled record, not as a substitute for an explanatory log, review or an approved retention process. Avoid overwriting earlier scenario work in a way that destroys the comparison trail.
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Make the route from inputs through calculations to outputs legible. Label input cells, formula cells and results; show units and sign conventions; and keep formulas consistent and understandable. Prefer a simpler construction when it communicates the calculation more clearly than an opaque formula. Document macros, queries, external links and non-obvious logic, and annotate complex sections where a reviewer needs context.
Inspect parts of the workbook that may not be visible in its normal presentation: hidden sheets, rows and columns, named ranges, external links, and cells or controls that affect an output. ICAEW’s The auditor’s review of management spreadsheets (2024) notes: “Unlike most IT systems, spreadsheets often lack a robust audit trail, making it difficult to track changes and understand who made them.” A tidy front sheet cannot compensate for unexamined logic elsewhere in the file.
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How to test formulas, assumptions and outputs
AI-generated calculations need independent checks before the model is relied on. ICAEW recommends testing proportionate to a workbook’s size, complexity and criticality, alongside peer review, controls and alerts. Preserve the test inputs, expected results, actual results, exceptions and resolution with the release.
Check data and calculations
- Confirm input completeness, accuracy, units, transformations and source reconciliation; check link and refresh status.
- Independently recompute or benchmark material calculations rather than treating the AI’s explanation as evidence.
- Reconcile important totals and natural balances, and confirm that flags and control checks work as intended.
- Inspect signs, units and formula consistency, especially where a mistaken convention could make a plausible-looking output misleading.
Test behavior under changed and invalid inputs
Run a base case and relevant upside, downside and stress cases with named assumptions. Change inputs deliberately and check that outputs move in a sensible direction. Test boundary, extreme, negative, missing or invalid inputs where relevant to the model’s use. Scenario analysis makes the changed assumptions and resulting output effects demonstrable; retain the exact scenario inputs and results rather than only the final chart or summary.
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How to review, approve and monitor the released model
Assign a human owner accountable for the model’s use and released version. Where appropriate to the risk, separate the preparer from a suitably capable reviewer who checks material logic and evidence. Track review comments, exceptions, remediation and approval; specify who may change source data, formulas, assumptions and released versions. Revisit the model after material changes in data, business conditions, markets or logic.
Version history and access restrictions can help identify or recover prior work, but neither proves the model is correct. The approval record should make clear what was reviewed, which exceptions remain or were resolved, and which workbook version was authorized for use.
How to choose version-history and collaboration features
No single spreadsheet platform is established here as the best choice or as sufficient to make a model audit-ready. When evaluating a platform or workflow, check whether it supports:
- Identifying and restoring prior versions.
- Seeing who changed what and when.
- Reviewer permissions and access controls.
- Retention and export of records.
- Connected-data handling and preservation of source snapshots.
- Compliance with the organization’s security and records policies.
ICAEW’s examples include SharePoint/OneDrive and Google Drive version history. The important control is the complete process around the feature: traceable inputs, an explanatory change record, suitable review and an identifiable approved release.
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