AI Month-End Intelligent Check

Problem

During month-end close, finance teams must check multiple ledgers for account balances, bank reconciliation, negative assets, accounting equations, and expense anomalies. Manual line-by-line review can miss issues, while rules may be scattered across periods, ledgers, and file formats.

How It Works

AI Month-End Intelligent Check structures the workflow into data intake, rule setup, execution, and reporting.
- Data intake: supports Excel, CSV, and OCR-dependent PDF or image vouchers; maps fields such as account code, opening balance, current period activity, and closing balance.
- Rule engine: includes bank_reconciliation, asset_negative, accounting_equation, expense_fluctuation, and completeness; supports natural-language rules for target accounts, thresholds, and ledger scope.
- Multi-ledger management: rules can apply to one, several, or all ledgers, with data partitioned by ledger and accounting period.
- Reporting: execution marks compliant, abnormal, and warning items; results can be rendered as Markdown, exported to Excel, or queried in natural language.

Boundaries

Initial rule setup and period data are required before checks run. PDF and image ingestion depend on OCR quality. expense_fluctuation uses a default threshold around 10%, and the detail ledger uses account, voucher, and date constraints to prevent duplicates. It is useful for repeatable month-end checklists, not a full audit or tax opinion.