A safe workflow for AI-assisted reconciliation

Reconciliation is rules-heavy and repetitive, which makes it a good candidate for automation. The aim is to speed up matching and investigation without weakening the control.

Split the work

1. Deterministic matching first. Exact amount, date and reference matches belong in rules or your ERP, not in a language model.
2. AI for the leftovers. Use the model to propose matches for fuzzy items: partial payments, grouped receipts, and narrations that differ between systems.
3. Human approval. A named reviewer approves every AI-proposed match above a set value and a sample below it.

Controls to keep

• Log the input, the model’s suggestion and the approver.
• Keep the reconciliation of totals independent of the AI: opening balance plus matched items plus unmatched items must equal the closing balance.
• Test on a past month with known answers and record the hit rate.
• Exclude sensitive fields, or use an approved enterprise tool with data protection terms.

Measure it

Track time to close, share of items auto-matched, share of AI proposals accepted, and errors found later. If acceptance is low, improve the rules and data before changing the model.