Accounting & bookkeeping
The ledger shows the balance. Review history shows the judgment.
Firms that keep books, close months, and audit clients can record expert judgment: what a transaction is, whether a variance matters, which items to test, and what the reviewer changed. Volume alone is not the asset. Source records linked to expert decisions and later results can form a dataset that is difficult to reproduce.
01Systems you already run
The raw material is already being recorded.
An assessment starts from the software you run today. It asks what those systems hold, never for the records themselves.
- General ledger and ERP
- QuickBooks Online, Xero, NetSuite, Sage Intacct
- Practice management
- Karbon, TaxDome, Canopy, CCH Axcess Practice
- Close and reconciliation
- BlackLine, FloQast, Trintech
- Audit workpapers
- CaseWare, CCH ProSystem fx Engagement
- Tax preparation and research
- UltraTax CS, CCH Axcess Tax, Lacerte, Checkpoint
- Payables and receivables
- BILL, Tipalti, Dext, Hubdoc
02Dataset opportunities
Where the learnable data sits.
Each opportunity follows a decision from input to outcome. That chain, not the number of records, is what makes data useful for training and evaluating AI.
01
Transaction coding with reviewer corrections
Bank and card transactions as first coded by a bookkeeper or a rule, the reviewer's correction, and the balance that survived close.
- Bank or card line
- Initial coding
- Reviewer correction
- Close sign-off
- Later reclass
Why it is hard to reproduce
A transaction or chart of accounts does not explain a reviewer's correction. Pairs of an initial answer and an expert's fix, linked to the final treatment, capture a review history that is difficult to reconstruct.
AI use cases
- Preference and reward data for bookkeeping agents
- Categorization evaluation sets by industry and entity type
- Fine-tuning on correction rationale where reviewers leave notes
02
Reconciliation and payables exceptions
Unmatched bank lines, duplicate or disputed invoices, short payments, and the investigation and entry that cleared each one.
- Exception raised
- Investigation
- Vendor or client follow-up
- Adjustment or write-off
- Cleared at close
Why it is hard to reproduce
Exceptions are the hard cases by definition. Each one records how a practitioner traced a discrepancy across systems and documents, the kind of multi-step reasoning that is difficult to generate synthetically with confidence.
AI use cases
- Agent environments for reconciliation and close tasks
- Tool-use traces across ledger, bank feed, and document systems
- Failure-case evaluation for finance agents
03
Month-end close workflows
Close checklists, task order, preparer and reviewer sign-offs, review notes, rework, and the time each step took.
- Close checklist
- Preparer work
- Review notes
- Rework
- Sign-off and lock
Why it is hard to reproduce
A close checklist alone omits the review decisions and rework. Notes linked to corrections and sign-off can explain how a particular close was completed.
AI use cases
- Long-horizon workflow training for finance agents
- Benchmarks for review completeness
- Process evaluation across client size and complexity
04
Audit sampling decisions and findings
Risk assessments, sample selections, test results, proposed adjustments, and whether management accepted them.
- Risk assessment
- Sample selection
- Testing
- Proposed adjustment
- Management response
Why it is hard to reproduce
Workpapers can connect risk assessments to testing decisions and findings. Reproducing that chain requires both professional judgment and the evidence from the engagement.
AI use cases
- Evaluation of audit-assistant judgment
- Misstatement and anomaly detection benchmarks
- Expert-calibration data for risk scoring
05
Tax research memos and client questions
Questions clients asked, the research path, the position taken, and later notices or amendments.
- Client question
- Research path
- Position memo
- Return treatment
- Notice or amendment
Why it is hard to reproduce
Public guidance states the rule. Research memos show how practitioners applied ambiguous rules to messy facts, and what happened when the position was tested.
AI use cases
- Fine-tuning and evaluation for tax research assistants
- Reasoning benchmarks grounded in real fact patterns
- Seed material for expert-reviewed synthetic scenarios
03Volume is not the test
What is usually not valuable on its own.
Lots of data is not the point. A record of an expert deciding, and of what happened next, is.
Ledgers and trial balances on their own
Posted balances describe a final state. Without the corrections and decisions behind them, they provide little evidence of the review process.
Receipts and invoices without coding history
Document images alone omit the accounting decisions. Linking them to coding, review, and resolution can add useful context.
Generic templates and checklists
A template or checklist describes an intended process. Completion and review history can show how practitioners actually used it.
Financial statements already filed
Public filings are available from other sources. Without a review or decision trail, they offer less differentiation.
04Rights and compliance
The sector rules for the dataset.
Rights reviewed before any outreach.
Last reviewed .
How we handle rights and privacy- 01
Professional confidentiality
Confidentiality duties depend on professional status, applicable state rules, and contracts. The AICPA Code's Confidential Client Information Rule applies to members in public practice and generally requires specific client consent, subject to exceptions.
- 02
Tax return information
For covered tax return preparers, 26 U.S.C. 7216 restricts use or disclosure of tax return information except as authorized by the statute and regulations. Consent requirements and exceptions appear in 26 CFR 301.7216-1 through 301.7216-3. Section 7216 provides criminal penalties; 26 U.S.C. 6713 provides related civil penalties. The statistical-compilation exception limits uses and bars sale or exchange except with transfer of the tax preparation business. De-identification alone is insufficient.
- 03
Ownership and reuse rights
Client records, firm workpapers, and third-party materials can carry different ownership and access rights. Contracts and professional rules may restrict reuse even where the firm owns a record. Permission needs to cover the categories included, including derivatives and review notes.
- 04
Engagement letters
Engagement letters and client agreements may restrict confidentiality, data use, and retention. A general opt-in clause may not satisfy Section 7216 consent requirements, particularly for Form 1040-series returns. Consent requirements depend on the return type, purpose, recipient, and required form.
- 05
Financial privacy and security
Financial records may contain personal information subject to privacy and security requirements. The FTC Safeguards Rule under the Gramm-Leach-Bliley Act (GLBA) applies to qualifying financial institutions under FTC jurisdiction, including tax preparation firms. It requires safeguards for covered customer information; compliance does not itself permit licensing.
05AI use cases
Training examples and evaluation tasks.
Linked source records, reviewer corrections, and close outcomes give finance teams examples for agent training and evaluation. Reconciliation histories show investigation steps across ledger, bank, and document systems. Consistent review notes explain why an entry changed and how the exception was resolved.
06Questions
What owners in this sector ask first.
- Which records should we assess first?
- Start with reviewer corrections, reconciliation exceptions, and close histories. Look for records that preserve the original entry, the expert change, its reason, and the final treatment.
- Why are reviewer corrections useful?
- A corrected record pairs a plausible first answer with an expert fix. That pair shows a model what changed and why. The ledger contains the final state; the review history explains how it was reached.
- Is a small firm's data useful?
- Several years of consistently recorded corrections in a focused client niche can supply detailed examples. Start with the depth of the review trail, its consistency, and the outcomes attached.
- What do we need for the first assessment?
- The systems you use, the period covered, approximate record counts, and a description of the decisions captured. The assessment uses dataset descriptions, with no upload step.
Find the AI opportunity in your records.
Start with a free assessment of your systems, the decisions recorded, and the outcomes linked to them. You keep ownership.