ALFA
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Clean a messy bank or ledger export and get the formulas written for you

Turn a CSV with merged headers, split descriptions, text-formatted numbers, and subtotal rows into a clean table you can pivot, with a check that nothing was lost.

Instead of an hour of find-and-replace, text-to-columns, and formula tinkering, you describe the table you want and let AI write the cleanup steps, either as a script it runs or as formulas you paste. The part that changes most is the check: AI writes a tie-out so you can prove the clean file still equals the original.

Time
Saves 10 to 30 min
About 45 min down to 12 min per export
Who does it today
Bookkeepers and staff accountants
Practice area
CAS and bookkeeping

Illustrative planning estimate, not a measured result. Assumes a 500 to 3,000 row bank, card, or general ledger export with the usual problems: header rows, page subtotals, amounts stored as text, debits and credits in separate columns, and descriptions split across lines. AI time includes checking the tie-out totals.

Traditional vs AI-native

Traditional

  1. 1Delete header junk
  2. 2Fix text-as-numbers
  3. 3Merge debit and credit
  4. 4Write formulas by trial and error
  5. 5Hope the total still ties

AI-native

  1. 1Upload a sample and describe the target
  2. 2AI writes the cleanup
  3. 3AI runs it and shows the result
  4. 4AI writes the tie-out
  5. 5You check the totals

Where you sit

You define what clean looks like and you check the tie-out against the source statement. The tool does the reformatting and the formula writing.

The traditional way

  1. Open the export and delete bank logos, headers, and page subtotal rows.
  2. Convert amounts stored as text, strip currency symbols, and fix negative numbers in parentheses.
  3. Combine separate debit and credit columns into one signed amount.
  4. Rejoin descriptions that wrapped onto a second row.
  5. Write the formulas to categorize, look up, or summarize, testing until they work.

Where it hurts

  • Every bank and every software export is messy in its own way.
  • Formula syntax eats time that should go to the actual analysis.
  • Rows get dropped in cleanup and nobody notices until a reconciliation fails.

The AI-native way

  1. 1You

    Show it the mess and the target

    Upload the file, or the first 30 rows if you are keeping the rest out of the tool, and describe the columns you want at the end: date, description, amount (signed), account, source row.

    Tools: ChatGPT or Claude with file analysis · Microsoft Copilot in Excel · Claude Code for repeat jobs

  2. 2AI

    Write and run the cleanup

    Tools that run code will write a short script, apply it, and give you the clean file. In Excel, ask for the formulas and a plain-English explanation of each so you can check them.

  3. 3AI

    Write the tie-out

    The cleanup always ends with a check: row count in and out, total of amounts before and after, and a list of any rows removed and why (subtotals, blank lines). If the totals do not match, it says so.

  4. 4You

    Check the totals yourself

    Compare the tie-out to the statement's own beginning balance, ending balance, and total activity. That comparison is yours, not the tool's.

What you need

  • The export (CSV or Excel)
  • A description of the target columns
  • The statement totals to tie to
  • An AI tool that can analyze files, or Copilot in Excel

Copy the prompt

Cleanup with a tie-out

Attached is a bank export. I need a clean table with these columns:
Date (YYYY-MM-DD) | Description | Amount (deposits positive, withdrawals negative) | Source row

Clean it by:
- removing header, footer, and page subtotal rows
- converting text amounts to numbers, including negatives shown in parentheses
- combining the Debit and Credit columns into one signed Amount
- joining any description that wrapped onto the next row

Then give me a tie-out:
- rows in, rows out, and every removed row with the reason
- sum of deposits and sum of withdrawals, before and after
- flag anything that does not tie

Also give me an Excel formula that returns the total withdrawals for a month entered in cell H1, and explain it in one sentence.

Example

Fictional data.

Raw export (excerpt)

FIRST STATE BANK  Account ****4471  Statement 08/01/2025 - 08/31/2025
Date,Description,Debit,Credit,Balance
08/02/2025,"ACH DEPOSIT STRIPE",,"2,410.00","14,882.19"
08/03/2025,"POS PURCHASE HOME DEPOT #0213",(311.46),,"14,570.73"
08/03/2025,"ONLINE TRANSFER TO
SAVINGS",(1000.00),,"13,570.73"
Page 1 Subtotal,,(1311.46),2410.00,

Clean table and tie-out (excerpt)

2025-08-02 | ACH DEPOSIT STRIPE                      |  2410.00 | row 3
2025-08-03 | POS PURCHASE HOME DEPOT #0213           |  -311.46 | row 4
2025-08-03 | ONLINE TRANSFER TO SAVINGS              | -1000.00 | rows 5-6

Tie-out: 212 rows in, 198 out. Removed 14: 2 header, 9 page subtotal, 3 wrapped lines joined.
Deposits 41,806.20 before and after. Withdrawals 39,114.07 before and after. Ties.

=-SUMIFS(C:C, C:C, "<0", A:A, ">="&H1, A:A, "<"&EDATE(H1,1))
Total withdrawals, as a positive number, for the month that starts on the date in H1.

Guardrails

  • Always ask for the tie-out, and always compare it to the statement's own totals.
  • Keep the original export untouched. Clean a copy.
  • Bank data identifies the client. Use an enterprise AI tool under a data agreement, or strip account and name details first.

What can go wrong

Rows silently disappear in cleanup.

The tie-out lists every removed row with a reason, and row counts in and out.

Date formats are misread (day and month swapped).

State the source date format in the instruction and spot-check dates near the start of the month.

Where this goes

Save the working instruction for each bank the client uses, and next month's cleanup is one sentence. From there it becomes the first step of automated categorization and reconciliation.

Demo

Demo video coming soon

Until then, the steps and the example above are the walkthrough.

Want to implement this in your firm?

Bring it to the weekly call. Members work through builds like this one together, with accountants who are already doing it.

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