Best AI for CFOs

Research notes on AI in finance work, by task

Index
W/P 12
Built
4 November 2026
Volume
1
This page
4 exhibits
  1. Best AI for CFOs
  2. Use cases
  3. Audit preparation

12of 12 workpapers

Audit preparation and evidence

How do AI products prepare audit evidence and documentation, and what must a person review before it is relied on?

Published . Vendor pages read 2 November 2026.

How AI products gather evidence, test controls, analyse full populations and draft disclosures for audits, what reviewers must check, and vendor questions.

Products listed
7
Makers in the SEC ticker file
0
Filing excerpts
2
Notes
17

Lead schedule.Data the makers in this workpaper name

One stroke per record, in fives. Systems named by 4 or more records; every system is in Appendix C.

The schedule scrolls sideways; the workpaper column stays in place.

W/PBanksNetSuiteERPSAPCardsIntacctWorkdayClaudeCRMQuickBooksDynamicsOraclePaymentsPayrollSalesforceNo data named
12Audit preparation14

Answer sheet.What the makers' pages in this workpaper answer

One stroke per record: solid where the maker's own page answers the question, dotted where the pages read are silent. A count of what the pages say, not a score.

W/PData it namesWhere a person reviewsModel or data handling
12Audit preparation3 of 76 of 72 of 7

1The work

AI products for audit preparation collect and organise evidence, test controls and full transaction populations, extract data from supporting documents and draft disclosure checklists, for companies' internal audit and control teams and for audit firms; a person reviews the work and owns the conclusion. The makers in this note describe agents for evidence collection and control testing, document extraction inside audit workpapers, analysis of every transaction for unusual activity, and assistants that answer from standards with a link to the source passage.

On the company side, audit preparation means producing what the auditors ask for (the request list): reconciliations, schedules, samples with support, control evidence, policies and memos. For a listed company, management also evaluates its internal control over financial reporting each year; the SEC's guidance for that evaluation is Release 33-8810.1 Internal audit and SOX teams test controls during the year and document the results.

On the auditor side, the work is planning and risk assessment, testing and documentation, then review. Analysis of whole populations, rather than samples, has become practical with software, and audit tools increasingly read documents and draft workpapers.

A person must stay in the loop for the judgment that evidence is sufficient and appropriate, for the evaluation of any exception or deficiency, and for sign-off. Evidence an AI agent collected is still evidence someone must check: that it is complete, that it came from the system it claims to, and that it supports the assertion. When a tool drafts a disclosure checklist or a memo, the reviewer is signing the conclusion, not the draft.

2Where AI is applied

Makers describe automated evidence collection and control testing, agents that review documents and extract key information inside an engagement, analysis of all transactions for unusual activity with explanations, citation-backed disclosure review, and assistants that answer from accounting standards and firm policy.

Exhibit 12.1.What the makers say, in their words, alphabetical by maker

  • Reduce manual evidence preparation with agents that review documents, extract key information and support testing workflows directly inside the engagement.8
    Caseware, Caseware Verity Agents
  • End-to-end agentic automation, built for the realities of audit9
    DataSnipper, DataSnipper AI, Alwin
  • Runs end-to-end testing and evidence verification across audit and advisory engagements, reviewed by practitioners.10
    Fieldguide, Field Agents
  • MindBridge analyzes 100% of the financial data in scope, surfaces unusual activity, and explains what drives risk11
    MindBridge, MindBridge, Autonomous Financial Oversight
  • Make auditors strategic partners with automated evidence collection, control testing, and sampling.12
    Optro (formerly AuditBoard), Optro AI, Optro Assistant
  • An AI agent that checks each client upload against the original request the moment it arrives. It looks at document type, period, completeness, and fit with the request description, and flags anything that doesn't match.13
    Suralink, Document Prescreen Agent
  • Apply the same questions to every file in a population, so the hundredth lease gets the same scrutiny as the first.14
    Trullion, Trulli

Source: the makers' own pages, read 2 November 2026; notes 8 to 14.

3Limits and control questions

Thresholds are a judgment, not a setting. The SEC staff's view on materiality is that "exclusive reliance on this or any percentage or numerical threshold has no basis in the accounting literature or the law."2 A tool that flags only what crosses a percentage leaves the qualitative items to a person.

COSO describes its Internal Control, Integrated Framework as originally issued in 1992 and refreshed in 2013.3 Control testing tools are only as good as the control descriptions they are given.

Where a product uses a generative model to draft text, NIST has published a separate profile on the risks particular to generative AI, NIST AI 600-1.4 Ask how each drafted conclusion links to the evidence behind it.

NIST's AI Risk Management Framework is a common reference for those questions. NIST says the framework is "intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems."5 Its first version is published as NIST AI 100-1.6

Vendor claims about AI are claims, not findings. The SEC has brought settled charges against two investment advisers "for making false and misleading statements about their purported use of artificial intelligence (AI)."7 A finance team that relies on a vendor's description should ask for evidence of what the feature does with its own data.

4What filers say

Exhibit 12.2.Filers on their own finance work

Companies describing AI in their own operations, from their SEC filings. A filing is not evidence that the filer uses any product on this page unless it names one.

  • Notwithstanding the use of AI in our application and with certain agent activities, we’ve yet to utilize AI within our financial reporting or internal control over financial reporting functions.16
    Linkhome Holdings Inc. (LHAI), Form 10-K filed 26 March 2026

Source: SEC EDGAR full-text search; note 16.

Exhibit 12.3.Finance-software filers on their AI products

Listed software companies describing their own AI products to investors. These are the makers' statements, made in filings.

  • We offer the only unified, AI-powered SaaS platform that brings customers’ financial reporting, sustainability management, and GRC together in a controlled, secure, audit-ready platform.17
    WORKIVA INC (WK), Form 10-K filed 19 February 2026

Source: SEC EDGAR full-text search; note 17.

5Products

Exhibit 12.4.Products addressing audit preparation and evidence, by the data they name

7 product records whose makers say they address this task, alphabetical by maker. Open a row for our summary, the data it names and the maker's words on review; the marks show which questions the maker's pages answer.

  1. Caseware Verity AgentsCasewareData it names: quoted.Where a person reviews: quoted.Model or data handling: not stated.
    Our summary
    AI agents for audit engagement work: planning, risk assessment, evidence preparation and disclosure review. The maker says reviewers can accept or override each suggestion.
    Data it names (our list)
    Financial data, Prior-year history, Controls, Supporting documents
    Where a person reviews (quoted)
    generating citation-backed suggestions auditors can review, refine, accept or override
  2. DataSnipper AI, AlwinDataSnipperData it names: not stated.Where a person reviews: quoted.Model or data handling: not stated.
    Our summary
    Agentic automation for audit and financial control work, built into the auditor's workflow. The maker states a human-in-the-loop design.
    Data it names (our list)
    Not stated on the pages read.
    Where a person reviews (quoted)
    We believe in creating AI-augmented workflows with humans in the loop
  3. Field AgentsFieldguideData it names: not stated.Where a person reviews: quoted.Model or data handling: not stated.
    Our summary
    AI agents for audit and advisory firms that run testing and evidence verification within engagements. The maker says practitioners review the agents' work.
    Data it names (our list)
    Not stated on the pages read.
    Where a person reviews (quoted)
    Planning arrives assembled and evidence-backed, ready for practitioners to review before fieldwork begins.
  4. MindBridge, Autonomous Financial OversightMindBridgeData it names: not stated.Where a person reviews: not stated.Model or data handling: not stated.
    Our summary
    Analytics that score the full population of financial transactions for unusual activity, used by finance and audit teams. The maker says it explains what drives each risk.
    Data it names (our list)
    Not stated on the pages read.
    Where a person reviews (quoted)
    Not stated on the pages read.
  5. Optro AI, Optro AssistantOptro (formerly AuditBoard)Data it names: quoted.Where a person reviews: quoted.Model or data handling: quoted.
    Our summary
    Governance, risk and compliance software with AI agents for evidence collection, control testing and risk mapping. The maker says the customer sets the agents' boundaries and approves outcomes.
    Data it names (our list)
    200+ integrations, ChatGPT, Claude, Copilot
    Where a person reviews (quoted)
    Your team sets the boundaries, governs and tunes the agents, and approves outcomes.
  6. TrulliTrullionData it names: quoted.Where a person reviews: quoted.Model or data handling: quoted.
    Our summary
    An AI accounting assistant that answers questions from standards, firm policies and documents such as leases, applying the same questions across a population. The maker says each answer links to its source passage.
    Data it names (our list)
    Accounting standards, Firm policies, Documents
    Where a person reviews (quoted)
    Every answer links back to the source passage, so reviewers can check the evidence before it goes into the file.

Source: the makers' own pages, read 2 November 2026; notes 8 to 15.

6Questions to put to a vendor

  1. Which evidence does the product collect itself, and from which systems, with what permissions?
  2. How does a reviewer see where each piece of evidence came from and when?
  3. Does it test full populations, and how are exceptions explained and routed?
  4. When it drafts a workpaper, checklist or memo, how is each statement tied to evidence?
  5. Has an external audit firm relied on its output, and with what review?
  6. Which model reads documents and drafts text, and is our data used for training?
  7. How are its agents governed: who sets their limits and approves their results?
  8. What does it keep as an audit trail of its own actions?

Notes

  1. U.S. Securities and Exchange Commission, Commission Guidance Regarding Management's Report on Internal Control Over Financial Reporting, Release 33-8810. www.sec.gov/rules/interp/2007/33-8810.pdf. Read 29 October 2026.
  2. U.S. Securities and Exchange Commission, Staff Accounting Bulletin No. 99: Materiality. www.sec.gov/interps/account/sab99.htm. Read 29 October 2026.
  3. COSO, Internal Control, Integrated Framework. www.coso.org/guidance-on-ic. Read 29 October 2026.
  4. National Institute of Standards and Technology, Generative Artificial Intelligence Profile, NIST AI 600-1. nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf. Read 29 October 2026.
  5. National Institute of Standards and Technology, AI Risk Management Framework. www.nist.gov/itl/ai-risk-management-framework. Read 29 October 2026.
  6. National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1. nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf. Read 29 October 2026.
  7. U.S. Securities and Exchange Commission, SEC Charges Two Investment Advisers with Making False and Misleading Statements About Their Use of Artificial Intelligence. www.sec.gov/newsroom/press-releases/2024-36. Read 29 October 2026.
  8. Caseware, AI for Audit & Accounting | Caseware. www.caseware.com/us/platform/ai. Read 2 November 2026.
  9. DataSnipper, DataSnipper AI. www.datasnipper.com/product/ai. Read 2 November 2026.
  10. Fieldguide, Build the Agentic Firm the Next Decade Demands – Fieldguide. www.fieldguide.io/. Read 3 November 2026.
  11. MindBridge, MindBridge AI | The Leader in Autonomous Financial Oversight. www.mindbridge.ai/. Read 30 October 2026.
  12. Optro (formerly AuditBoard), AI for GRC | Optro's Agentic System of Action. optro.ai/platform/ai. Read 31 October 2026.
  13. Suralink, Document Prescreen Agent. www.suralink.com/document-prescreen-agent. Read 2 November 2026.
  14. Trullion, Trulli: AI Agent for Accounting & Audit | Trullion. trullion.com/products/ai-agent-trulli/. Read 1 November 2026.
  15. Optro (formerly AuditBoard), Optro (Formerly AuditBoard): AI and LLM Information. optro.ai/ai-info-for-llms. Read 31 October 2026.
  16. SEC EDGAR, Linkhome Holdings Inc., Form 10-K filed 26 March 2026. www.sec.gov/Archives/edgar/data/2017758/000121390026034538/ea0281818-10k_linkhome.htm. Read 29 October 2026.
  17. SEC EDGAR, WORKIVA INC, Form 10-K filed 19 February 2026. www.sec.gov/Archives/edgar/data/1445305/000144530526000016/wk-20251231.htm. Read 29 October 2026.

Other use cases: Forecasting, Accounts payable, Expense audit, Collections and cash application, Reconciliation, Close automation, Variance analysis, Contract and lease data, Tax research, Treasury and FX, Payment anomalies and fraud.