Month-end close checklists, reconciliations and workpapers
Records of how finance teams close the books each month: close checklists, account reconciliations with supporting detail, journal entries and reviewer sign-offs. Labs use them to train agents for close and reconciliation work, where the steps and review standards matter as much as the numbers.
What a record contains
One close period: the checklist with owners and due dates, reconciliations per account with explanations of differences, journal entries with support, and review notes.
| Field | What it holds |
|---|---|
period | Month being closed |
checklist[] | Task, owner role, due date, completed date |
reconciliations[] | Account, balances, difference, explanation, review status |
journal_entries[] | Lines, memo and supporting document |
review_notes[] | Reviewer questions and responses |
{
"period": "2024-06",
"reconciliations": [
{"account": "1210 Accounts receivable", "gl_balance": 412880.15,
"subledger_balance": 410905.15, "difference": 1975.00,
"explanation": "Customer prepayment posted to AR, reclass in JE-0612",
"review_status": "approved"}
],
"review_notes": [{"by": "controller_role", "note": "Attach the remittance for the reclass."}]
}How AI labs use it
- Reconciliation agents
- Real differences with the explanations teams wrote.
- Review support
- Reviewer notes show what gets questioned.
- Evaluation
- Check a model’s reconciliation against the signed-off one.
Typical preparation requirements
Agreed with the supplier before any work begins. Typical requirements include:
- Bank account numbers and personal data removed
- Payroll and compensation details excluded
- Amounts consistently scaled where needed, so reconciliations still balance
Every dataset has a documented owner and confirmed licensing rights. See data governance on sourcex.si.
What makes a strong package
- Several years of consecutive periods
- Written explanations for differences
- Reviewer sign-offs
Compared with public datasets
Public sets such as SEC EDGAR filings and FinQA are useful references, but limited as enterprise training data. The finance and accounting category page compares them with licensed data.
Who typically holds it
- Mid-sized companies
- Private-equity-backed businesses
- Outsourced accounting firms, with clients’ permission
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Can amounts be disguised?
Amounts can be scaled consistently so the work still reconciles. Scaling can be reversed if any true amount is known, so sensitive figures may need further masking.
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