Account Reconciliation in Excel With ChatGPT Prompts

Why I tested ChatGPT on account reconciliations

Reconciliations are the part of accounting that quietly eats time. Matching bank feeds against the general ledger, hunting down timing differences, and tracing stale checks — it’s thankless but essential.

For years, I did this manually in Excel. I’d lean on VLOOKUP, COUNTIF, and conditional formatting, and it worked… until the month got busy, the data got messy, and I was left squinting at mismatched rows.

So I tested a new approach: account reconciliation in Excel with ChatGPT.

Could ChatGPT generate the formulas, VBA macros, and reconciliation checks I normally build by hand? Could it actually help accounting and finance teams work more efficiently without sacrificing control?

Here’s what I learned.

Setting up the project for ChatGPT

The first lesson: ChatGPT only knows what you tell it.

I treated it like onboarding a junior analyst. Instead of saying “reconcile bank to GL,” I explained the structure:

“I’m reconciling a cash account. I have two tables in Excel:

• Bank transactions in A2:C200 (Date, Description, Amount).
• General ledger transactions in E2:G200 (Date, Reference, Amount).
  I need to identify exact matches, timing differences, and missing items.”

That setup was everything. Without it, ChatGPT guessed wrong. With it, the formulas it produced were almost plug-and-play.

This is why using ChatGPT for accounting tasks in Excel isn’t about magic. It’s about precision in how you prompt.

First attempt: exact match reconciliation

I started simple: flagging transactions that matched exactly between bank and GL.

ChatGPT suggested:

=IF(COUNTIF(G2:G200,C2)>0,”Match”,”Unmatched”)

It worked for identical amounts, and instantly cut down noise. But reconciliations are rarely that clean. Dates slip. Fees creep in. Adjustments get booked late.

That meant I needed something more flexible.

Building timing-difference checks

Timing is where reconciliations get tricky. A deposit might clear five days later in the bank than it’s recorded in the GL.

So I asked:
“In Excel 365, write a formula that checks if the amount in C2 matches an amount in G:G within 5 days of the date in A2.”

ChatGPT suggested:

=LET(txnDate,A2, txnAmt,C2, matchRow,FILTER(G2:G200,(ABS(E2:E200-txnDate)<=5)*(G2:G200=txnAmt)), IF(COUNTA(matchRow)>0,”Timing Difference”,”Unmatched”))

The first run gave me an error. I pasted the error back, asked for a fix, and within two iterations had a working formula that flagged near-matches.

That feedback loop was the real win. Automating bank reconciliation in Excel with ChatGPT isn’t about one perfect formula. It’s about iterating until Excel stops complaining and the logic makes sense.

Automating reconciliations with VBA

Formulas got me partway there. But account reconciliations benefit from automation.

Prompt:
“In Excel VBA, write a macro that compares bank amounts in column C to GL amounts in column G. Highlight unmatched bank transactions in red and unmatched GL transactions in yellow.”

ChatGPT returned runnable code. After tweaking sheet names, it worked. Rows lit up — red for deposits missing in the GL, yellow for uncleared checks.

This wasn’t code I’d ship to production without review. But as a draft, it saved me 30 minutes of writing loops manually. And every iteration got sharper.

Lessons from testing ChatGPT on reconciliations

1. Project setup is non-negotiable. The clearer the data ranges, the cleaner the outputs.
2. Iteration is expected. Copy errors back into ChatGPT. It learns from your workbook’s quirks.
3. Transparency beats shortcuts. I always asked it to explain each formula. That made it easier to audit later.
4. Judgment still belongs to me. ChatGPT can’t decide if a stale check should be written off. That’s finance, not syntax.

Why this matters for FP&A

In FP&A, credibility starts with reconciliations. If the GL doesn’t tie to the bank, no forecast will be trusted.

By testing account reconciliation in Excel with ChatGPT prompts, I saw two big gains:

• Faster turnaround on the grunt work.
• Cleaner, auditable formulas I could reuse month after month.

The threat is real: analysts who paste outputs blindly risk black-box reconciliations they can’t explain. That’s a credibility killer.

But the reward is bigger: analysts who use ChatGPT thoughtfully get speed without losing transparency. They spend less time firefighting mismatches and more time explaining what those mismatches mean for cash flow and forecasts.

Closing thought

Testing ChatGPT on reconciliations felt like training a new hire. It didn’t nail it on the first try. But with context, feedback, and corrections, it became a useful partner.

It didn’t remove the reconciliation grind. But it turned hours into minutes — and gave me cleaner logic I could explain in the boardroom.

And here’s the shocker: in the future, analysts won’t be judged on how fast they can reconcile by hand. They’ll be judged on how well they can prompt.