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TUE · NOV 3 · EDITION |
Build a bank transaction categorization workflow with AI
This workflow replaces the manual pass of matching bank rows to a chart of accounts one by one - a task that scales badly as transaction volume grows. It is built for bookkeepers and accounting staff who process monthly bank data and want the AI to handle the obvious categorizations while surfacing the genuinely ambiguous ones for human review. The whole first pass takes under five minutes of active work.
BUILD WITH AI
Build an AI workflow for bank transaction categorization
Export your bank transactions to CSV, clean out any bank-added formatting rows, and confirm your chart of accounts is in a text list you can paste into a prompt. The whole first pass takes under five minutes of active work.
1 |
Export the bank statement for the period as a CSV with at minimum these columns: date, description, amount, debit/credit indicator. |
2 |
Remove any header rows, subtotal rows, or bank branding rows so the file is clean tabular data. |
3 |
Open your general-purpose AI assistant and upload or paste the cleaned CSV along with your chart of accounts list. |
4 |
Paste this prompt: 'You are a bookkeeper. Below is a CSV of bank transactions and a chart of accounts. For each transaction row, assign the single most appropriate account name from the chart of accounts. If the correct account is ambiguous or the transaction appears to be a split, personal, or intercompany item, set the Confidence column to LOW and leave a one-sentence note in the Reason column. |
5 |
Download or copy the returned CSV and open it in a spreadsheet. |
6 |
Filter for Confidence = LOW and review each flagged row against supporting documents or client notes; correct the Account value manually. |
7 |
Reconcile: confirm the row count matches the original export and the sum of amounts matches the bank statement closing figure. |
8 |
Import the verified CSV into your general ledger using the standard import template for your accounting platform. |
WORKED EXAMPLE
In practice
A 143-row CSV of business checking transactions for the month just ended, exported from the bank portal. The firm's chart of accounts has 38 accounts, copied from the client's QuickBooks account list.
What came back. The model returned a 143-row CSV with 129 rows marked HIGH and 14 marked LOW. The HIGH rows covered recurring vendors - payroll processor, office supply merchant, utilities - correctly. Two of the LOW rows were flagged because the merchant name was a person's name rather than a business, which the model correctly noted as a possible owner draw; one row was a large round-number transfer the model flagged as a possible intercompany item. One HIGH-confidence row assigned 'Meals and Entertainment' to a charge that was actually a software subscription - the vendor name included a food word.
How it was checked. The reviewer filtered for LOW, corrected 11 of 14 flagged rows, and confirmed the two owner-draw items with the client. A sort by amount on the HIGH rows surfaced the miscategorized software subscription, which was corrected before import. Row count matched 143; sum of amounts matched the bank
A constructed example. The prompt is usable as written; the figures show the shape of a result, not a measured one.
WHEN TO USE IT
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Next issue: more of the tools and shifts reshaping how accounting gets done.
Forward this to the partner still doing it the old way.
An original workflow written for practitioners. Replicate it in a sandbox first; nothing here replaces your review.
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