Review AI-assisted bookkeeping

Direct answer

Review flagged exceptions and sample transactions the bookkeeping tool did not flag. Compare each suggestion or posting with the source document, chart of accounts, and review policy. Check whether the tool only suggests entries or can post them without approval. A sample helps find silent errors, but does not certify all entries as correct.

Why this question comes up

An exception queue only shows what the tool recognized as uncertain. A separate sample can expose mistakes in items it treated as routine. The review also needs the tool’s posting permissions and a record of what people approved.

MosoFin can help inspect connected records through a read-only QuickBooks review. It does not code or post bookkeeping entries; provide the external tool’s suggestions and approval log if they are not in the workspace.

Records to gather

• AI suggestions and posted transactions for the period
• Receipts, invoices, bank records, and the chart of accounts
• Exception queue and confidence indicators, if the tool provides them
• Approval log and a record of who or what posted each entry
• Prior review results measured with a comparable method

Review workflow

1. Define the population. Record the period, transaction types, and which items received AI suggestions or automatic posting. Separate pending from posted entries.
2. Review exceptions and a sample of unflagged items. Document the sample method and size. If confidence scores exist, include high-confidence items; otherwise sample from the unflagged population.
3. Check each item against evidence. Compare the source document and business purpose with the chosen account, amount, tax treatment, entity, and period. Send ambiguous cases to a qualified reviewer.
4. Prioritize consequential errors. Look closely at duplicate entries and classifications that change assets, expenses, tax, or financial-statement periods. Do not assume a round amount is inherently suspicious.
5. Check access and approvals. Compare the tool’s write permissions with the approval policy, then inspect the activity trail for entries actually posted or changed.
6. Report results with limits. Show errors over the sampled population, material exceptions separately, and the sampling method. Compare periods only when the populations and methods are similar.

What a useful answer should include

• Population, exception count, sample size, and selection method
• Errors by type and financial impact, with source evidence
• Suggestions versus automatic and human-approved postings
• Permissions and any approval-policy exceptions
• Missing logs or documents that limit the conclusion

Common failure modes

• Reviewing only flagged entries. The tool may not flag a confident mistake.
• Reporting one accuracy percentage without a method. A risk-selected sample and a random sample answer different questions.
• Treating a suggestion as a posting. Check the activity trail before claiming a ledger change.
• Assuming connected financial data includes the AI log. The suggestion, confidence, and approval records may live in a separate tool.