AI Bank Reconciliation

Reconciliation answers a simple question: does the cash the books say we have agree with what the bank says, and if not, why not? Traditionally an accountant ticked off matching items and listed the differences. Those include outstanding checks, deposits in transit, bank fees and interest not yet recorded, and errors. Software now does most of the matching. Xero and QuickBooks Online suggest matches as transactions arrive. Close-management tools such as BlackLine, Trintech and FloQast handle high volumes across many accounts and entities. Matching usually runs in passes. Exact matching pairs items with the same amount, date and reference number. Fuzzy matching loosens those tests. It accepts amounts within a tolerance, dates within a window to allow for clearing delays, and descriptions that are similar rather than identical. The bank may show 'ACME CORP PMT 88341' while the ledger says 'Acme Corporation - Inv 88341.' Machine learning can rank candidate matches using patterns learned from matches people approved in the past. The harder cases are not one-to-one. A payment processor deposits one net payout covering hundreds of sales minus fees and refunds, so one bank line matches many ledger lines. A customer may pay several invoices with one transfer, or pay only part of one invoice. Payroll can hit the bank as separate debits for net pay, taxes and benefits, all against one journal entry. Tools handle these with one-to-many and many-to-one matching. They are often helped by importing the processor's payout report or by routing payouts through a clearing account. A common misconception is that a high auto-match rate means the reconciliation is done. Unmatched items and low-confidence suggestions are where errors and fraud show up, and a wrong auto-match can hide a problem. Every exception needs investigating, documenting and signing off by a reviewer.