Training data for finance and accounting AI
Finance and accounting AI is trained on real bookkeeping work: transactions as coded and corrected by accountants, reconciliations with their exceptions, month-end close workpapers, working spreadsheets and the reviewer sign-offs that approved each step, ideally across several fiscal years. SourceX sources these records from accounting firms and finance teams running NetSuite, QuickBooks, Xero, Sage Intacct or SAP, with client confidentiality reviewed and names, account numbers and tax IDs de-identified before delivery.
Dataset types to start with
- Accounting and reconciliation workflows
GL coding with corrections, bank and card reconciliations, accruals and close checklists show how an accountant matches, codes and resolves exceptions, and the posted result is a label the agent's output can be checked against.
- Real-world spreadsheets and financial models
Much finance work happens in workbooks between systems: reconciliation templates, accrual schedules, variance analyses and models. Real files with live formulas teach an agent to read, extend and check them.
- Human feedback and QA-scored work
Preparer-reviewer sign-offs, review notes and adjustments made at review record what an experienced accountant rejects and why, a direct grading signal for agent outputs.
- Enterprise workflow and task execution histories
Close and payables processes span the ERP, bank portals, approval tools and email. Connected histories show the order of steps, who approved what, and how exceptions were routed and closed.
Why this data is hard to get
Accounting data is client-confidential
Ledgers and workpapers are among the most sensitive records a business keeps. Accounting firms hold them for clients under engagement terms and professional confidentiality duties, so the firm alone usually cannot license them without client authorization.
Public financials show results, not the work
Filings and published statements are aggregates. The transaction-level coding, matching logic, judgment calls and adjustments that produced them never leave the ledger, and synthetic ledgers miss the messiness of real bank feeds.
Each company codes differently
Charts of accounts, cost centers, materiality thresholds and close calendars differ by company. A label means little without the account structure, policies and ERP configuration it was applied under.
Corrections overwrite the trail
Reclassifications, reversed entries and edits during review are the most informative records, but exports often show only the final posted state unless audit history is requested.
Labels hide in corrections
In accounting data, the most useful labels are the changes. A transaction auto-coded by a bank rule and then reclassified by a senior accountant carries more signal than a long run of entries coded correctly the first time. An accrual adjusted at review shows the judgment the agent will need. Ask for audit history, not only posted balances: original and corrected coding, reversed and re-entered journals, and review notes on workpapers. Without it, the data teaches an agent to reproduce bank rules.
Context makes the labels interpretable. The same vendor charge can be expensed in one company and capitalized in another, and both are correct under their own policies. Ask for each company's chart of accounts, capitalization and materiality policies and close calendar with its records, and condition training on them rather than pooling all coding into one target.
Matching data to the task
Each kind of finance agent draws on different records. A transaction-coding model needs long ledger histories with corrections. A reconciliation agent needs bank and card statements paired with ledger entries and the exception log. A close assistant needs the checklist, the workpapers, their review trail and the order in which steps were completed. A spreadsheet agent needs real workbooks, ideally with the request that prompted each change. A program often combines two or three of these; say in the request which tasks come first.
Specifying a finance data request
State the systems, industries, entity sizes, currencies and fiscal years. Say whether you need multi-entity groups with intercompany eliminations, and whether audit adjustments matter. Agree early how amounts, dates and counterparties should be treated, because de-identification choices can break the matching logic you are trying to learn. For evaluation, reserve the most recent closed periods, and check that the same entity's earlier years do not leak recurring entries into the test set. SourceX matches the spec against accounting firms and finance teams whose records fit, and you review a manifest and sample before licensing.
What good data looks like
- Each transaction carries its original coding, any later reclassification, and who changed it and when.
- Reconciliations include open and reconciling items, their aging and how each was cleared, not only the final balances.
- Spreadsheets keep live formulas, links between sheets and the version history from preparation to review.
- The chart of accounts, coding policies and close calendar for each period are delivered with the records.
- Counterparty names, account numbers and client identities are pseudonymized consistently across the ledger, bank data and workpapers.
- Several consecutive fiscal years are covered, including year-end, audit adjustments and system migrations.
What is a financial AI agent, and what work does it do?#
A financial AI agent is software built on a language model that carries out multi-step finance work, such as coding transactions, matching bank lines or drafting close entries, by reading records and taking actions in finance systems rather than only answering questions. Finance AI agent and financial AI agent are used for the same thing; in accounting teams the term usually means an agent working inside the ERP, bank portals and spreadsheets.
What separates an agent from a chatbot is that it acts. The table shows the common tasks, the decision a person typically keeps, and the failure each one is most prone to. It is editorial guidance drawn from how close and payables work is usually reviewed, not a product survey.
| Task | What the agent does | Decision a person usually keeps | Failure to watch for |
|---|---|---|---|
| Transaction coding | Proposes account, cost center and tax code for new bank and card lines | Reclassifying coding that a reviewer already approved | Applying a bank's default category instead of company policy |
| Bank and card reconciliation | Matches statement lines to ledger entries and flags exceptions | Writing off or clearing unmatched items | Forcing a match on similar amounts that are different transactions |
| Accounts payable | Reads invoices, checks them against purchase orders and receipts, prepares payment batches | Releasing payments and changing vendor bank details | Paying a duplicate or a fraudulent vendor change |
| Month-end close | Drafts accruals and recurring journals and works through the close checklist | Posting judgmental entries and signing off the close | Rolling forward last month's estimate without new evidence |
| Variance and flux analysis | Explains movements between periods using ledger detail | Accepting the explanation as the reason given to management or auditors | Plausible explanations that the transactions do not support |
Which finance tasks can an agent do alone, and which need approval?#
Let an agent act alone only on reversible, low-value, rule-bound steps, and require a person's approval for anything that moves money, changes master data or records a judgment. FINRA's 2026 oversight report lists agents acting autonomously without human validation and approval, acting beyond the user's authority, and multi-step reasoning that is hard to trace among the risks of AI agents, and names human-in-the-loop oversight as a consideration for firms.
A practical way to set the line is three levels of autonomy, assigned task by task rather than for the agent as a whole.
- Suggest: the agent prepares a coding, match or entry and a person accepts or rejects it. Start every new task here.
- Act with approval: the agent posts or schedules the action, but it waits in a queue until an authorized person approves. Use this for journal entries, payment batches and anything above a materiality threshold the controller sets.
- Act and report: the agent completes the step and logs it for after-the-fact review. Reserve this for steps that are easy to reverse and whose error rate on held-out periods has stayed within an agreed tolerance.
- Never delegate: releasing payments to new or changed bank details, changing approval limits or user access, and signing off a period. These are control points, not tasks.
What controls do auditors and regulators expect around a finance agent?#
Expect the agent to be treated as part of the control environment: every action it takes has to be traceable, authorized and reviewed like a person's. For US public companies, Section 404 of the Sarbanes-Oxley Act led the SEC to require an annual management report on internal control over financial reporting, including management's assessment of its effectiveness, so an agent that posts entries falls inside that assessment.
Broker-dealers have a second layer: FINRA has reminded member firms that its rules are intended to be technology neutral and apply to generative AI the same way they apply to any other tool, whether the firm builds it or uses a third party's. The NIST AI Risk Management Framework and its Playbook are voluntary and organized around four functions, Govern, Map, Measure and Manage.
This is general information, not legal or audit advice; counsel and the company's auditors review the specific setup. In practice, reviewers tend to ask for the items below.
- A log of every action the agent took, with the inputs it read, the output it produced and who approved it.
- Separate identities and permissions for the agent, so it cannot both prepare and approve the same entry.
- A documented scope: which entities, accounts, amount limits and systems the agent may touch.
- Evidence that each autonomy level was tested before it was granted, and a schedule for re-testing after policy or system changes.
- A named owner who can switch the agent back to suggest-only when error rates or exceptions rise.
Sources for this section
- FINRA's blog on AI agents says agents may be less transparent and predictable than traditional supervision assumes. Source
- FINRA's 2026 report lists agent autonomy without human validation, acting beyond the user's authority and hard-to-trace multi-step reasoning as risks, and human-in-the-loop oversight among considerations. Source
- SEC rules adopted under Sarbanes-Oxley Section 404 require an annual management report on internal control over financial reporting, including an assessment of its effectiveness. Source
- FINRA's rules are intended to be technology neutral and apply to generative AI tools whether a firm builds them or uses a third party's. Source
- The NIST AI RMF Playbook is voluntary and covers four functions: Govern, Map, Measure and Manage. Source
Questions buyers ask
Can accounting firms license client bookkeeping data?
Only with client authorization, or where engagement terms clearly allow it, because firms hold client ledgers under confidentiality duties and clients usually own the records. SourceX checks this before any client's books are offered, and the check can narrow the clients included. Tax work is stricter: in the US, Internal Revenue Code section 7216 generally bars tax return preparers from using or disclosing information furnished for return preparation for other purposes without the taxpayer's consent.
How is sensitive financial data de-identified?
Customer, vendor and employee names, bank and card account numbers, tax identifiers and addresses are replaced, usually with consistent pseudonyms so matching still works across the ledger, bank statements and workpapers. Amounts and dates are often kept, since the accounting logic depends on them; whether they are perturbed or shifted is agreed during scoping. Free-text memos and attached invoices are reviewed too.
How do I evaluate a finance agent on historical periods?
Hold out recent periods. Give the agent the period's raw inputs, such as bank transactions, open items and the prior close, and compare its coding, matches and journal entries with what was posted after review. Score exceptions separately from routine matches, since that is where errors cost most, and treat accepted alternatives as correct where policy allowed either treatment.
How many fiscal years of accounting history are typical?
Partners usually hold several fiscal years, and the exact span varies by partner. Several consecutive years let a model see year-end entries, audit adjustments and policy changes, not just routine months. Older years may sit in a legacy system with a different chart of accounts, so ask for the account mapping between systems if your timeframe crosses a migration.
Can I request data from a specific ERP or industry?
Yes. Name the systems, such as NetSuite, QuickBooks, Xero, Sage Intacct or SAP, and the industries, entity sizes and currencies you need. Industry matters: revenue recognition in software differs from inventory costing in distribution. What can be sourced then turns on which firms and finance teams keep such records and will license them.
Tell us what you are building
Describe the model or agent, the tasks it must handle, and the volume, format and permitted use you need. SourceX will match it to partner data.
Updated 3 October 2026.