Finance and accounting data for AI training

Finance and accounting data shows the work behind the numbers: invoice coding, approvals, reconciliations, close checklists and financial models. AI labs license it to train agents for accounts payable, month-end close and planning, work that public filings don’t show.

Last updated October 3, 2026

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Listings

Data	Typical sources	Modality	Availability
Accounts payable invoices with coding and approvals Vendor invoices with GL coding, approval chains, exceptions and payment status.	NetSuite, QuickBooks, Xero	Documents and structured data	On request
Month-end close checklists, reconciliations and workpapers Close task lists, account reconciliations, journal entries and reviewer sign-offs.	BlackLine, FloQast, NetSuite	Spreadsheets and structured data	On request
FP&A models, budgets and forecasts with versions Financial models, budget cycles and forecast revisions, with assumptions and comments.	Excel, Google Sheets, Anaplan	Spreadsheets	On request

What’s included

Vendor invoices with GL coding, approvals and exceptions
Close checklists, reconciliations and journal entries with reviewer sign-offs
Budgets, forecasts and operating models with versions and commentary

Public datasets and licensed data

Public finance datasets are built from filed reports, so they show final numbers, not the internal work behind them. Licensed data adds transaction-level coding decisions, reconciliations and the review steps finance teams follow.

Public dataset	Released	What it contains	Limits for enterprise use	License
SEC EDGAR filings	US Securities and Exchange Commission	Public company filings, with indexes from 1994 to today, free to access.	Reported numbers and disclosures, not the internal work behind them.	Public
FinQA	UC Santa Barbara, J.P. Morgan and others, 2021	8,281 question-and-answer pairs over S&P 500 earnings reports (1999–2019), written by finance professionals.	Built on published reports.	MIT
TAT-QA	National University of Singapore and others, 2021	16,552 questions over tables and text from 182 real financial reports.	Built on published reports.	CC BY 4.0

Preparation and rights

Preparation requirements are agreed with each supplier before any work begins. For this kind of data they typically include:

Removing bank account numbers, tax IDs and signatures
Excluding payroll and compensation details unless specifically scoped
Scaling amounts consistently so the work still reconciles. Scaling can be reversed if any true amount is known, so sensitive figures may need further masking.
Getting each client’s permission when an accounting firm is the source, since client data belongs to the client

Every dataset has a documented owner and confirmed licensing rights, and is delivered only after an executed agreement and the supplier’s authorization. See data governance on sourcex.si.

Use cases

Questions

Can real financial figures be hidden?

Amounts can be scaled by a consistent factor so reconciliations and models still work. Scaling can be reversed if any true amount is known, so sensitive figures may need further masking.

Which systems does finance data come from?

NetSuite, QuickBooks, Xero, SAP, BILL and Coupa for transactions, BlackLine and FloQast for close work, and Excel or Google Sheets for models.

Need finance and accounting data?

Describe what you need: domain, volume, history, format and licensing terms. SourceX looks for companies that hold it and can license it.

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