|
TUE · SEP 15 · EDITION |
Build a bank transaction categorization workflow with AI
Manual transaction coding is slow and inconsistent, especially when one bookkeeper is working through a full month of rows at month-end. This workflow is for bookkeepers and accounting ops leads who want the model to handle the obvious matches and surface only the uncertain ones for human review, using their own chart of accounts as the guardrail. Set it up once and it runs the same way each period.
BUILD WITH AI
Build a bank transaction categorization workflow with an AI assistant
This workflow hands the first-pass sort to an AI assistant and routes everything uncertain to a human queue. Set it up once with your chart of accounts and it repeats cleanly each period.
1 |
Export the month's bank statement as a CSV with columns: date, description, amount, and a blank 'category' column. |
2 |
Open your chart of accounts and copy the account codes and names you want the model to work from - include only the accounts relevant to this bank account. |
3 |
Open your general-purpose AI assistant and paste the following prompt, replacing the bracketed sections with your actual data: "You are a bookkeeper categorizing bank transactions. Use only the account codes listed below. For each transaction row I provide, return a table with these columns: date, description, amount, assigned_account_code, assigned_account_name, confidence (high or low), and |
4 |
Paste the CSV rows (without the header) at the end of the prompt and run it. |
5 |
Copy the model's output table into a spreadsheet and filter for all rows where confidence is 'low' - review and resolve each of those manually before touching anything else. |
6 |
Spot-check ten rows marked 'high' confidence against the original bank statement to verify the categories are correct. |
7 |
Correct any errors in the spreadsheet, then import the finalized category column back into your accounting system using its standard import or journal entry process. |
8 |
Note any recurring miscategorizations and add a clarifying rule or example to the prompt for the next month's run. |
WORKED EXAMPLE
In practice
A bookkeeper has 310 transactions from a business checking account for the month just ended. The file is a CSV exported from the bank portal. The firm's chart of accounts has 22 relevant codes.
What came back. The model returned a 310-row table in under a minute. It flagged 18 rows as low confidence, mostly payments to general contractors where the description gave no clear indication of job materials versus professional fees. One high-confidence row was wrong: a payment to a tool supplier had been categorized as 5010 Office Supplies rather than 5070 Job Materials because the merchant name contained the word 'supply.'
How it was checked. The bookkeeper compared ten high-confidence rows side by side with the original bank statement PDF, found the tool supplier error, corrected it in the spreadsheet, and noted the merchant name as an example to add to next month's prompt.
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
| WHEN NOT TO
|
SPONSORED
GO DEEPER
Go deeper
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.
How accountants are using AI, automation and smarter workflows to close faster, audit cleaner, and free up time for real work.
Accounting Stack · Audit Friendly Data · Remote A&F Jobs
Audit Friendly · modernaccounting.ai