Why this is a worked example, not a testimonial
Let's be upfront. This is a representative scenario, not a named client. We've built it from the workflows accounting firms actually run at month-end, and we show every assumption so you can swap in your own figures. We think that's more useful than a polished quote you can't verify. The math below is a model, clearly derived, not a claimed customer result.
Manual document handling is a well-documented industry bottleneck. Ardent Partners found that average accounts-payable invoice processing takes 17.4 days, versus just 3.1 days for best-in-class teams (Ardent Partners, 2025). Different document, same lesson: keying data by hand is slow, and the firms that fix it pull far ahead.
The starting point: the manual workflow
Picture a typical mid-sized practice serving small businesses across retail, hospitality, and professional services. Every month, client bank statements land as PDFs, scans, and the occasional phone photo, one format per bank. Two staff members open the month by keying transactions into Excel, line by line, so the team has clean data to reconcile against.
The work is slow and easy to get wrong. A single mistyped balance can cascade into hours of reconciliation troubleshooting. Worse, it doesn't scale: taking on new clients means more late nights, not more margin. For a firm like this, the document itself is the bottleneck.
The assumptions (shown on purpose)
Here are the round, plausible numbers this model runs on. They're deliberately conservative and easy to adjust:
- ~40 client statements processed each month.
- ~150 transactions per statement on average, so roughly 6,000 rows/month to enter.
- 2 staff handling the keying.
- A realistic manual keying rate of ~100 rows/hour, including reading, typing, and basic checking.
- Post-automation, a human still reviews each statement: budget ~3 minutes per statement to scan flagged exceptions and confirm totals.
If your firm runs heavier, double the statement count. If your statements are shorter, halve the rows. The structure of the math holds either way.
The workflow with Extraly
In this model, manual keying is replaced with Extraly's bank statement to Excel conversion. The loop is simple: upload each client's statements, let the AI detect the bank format and extract every transaction, then download a consistently structured file. Because the columns are identical regardless of the originating bank, one reconciliation template works on the first pass, with no re-mapping per client.
For clients whose data feeds other software, the team exports to CSV and imports straight into their accounting platform. The point isn't to remove the human. It's to let the human verify exceptions instead of typing everything. You keep control of the data; you skip the tedious part. For the broader category, see what AI data extraction is.
The modeled result: showing the math
Now the derivation, step by step, using the assumptions above. This is where the headline number comes from, so you can audit it.
Manual effort. 6,000 rows / 100 rows per hour = ~60 hours/month of keying, split across 2 staff. That's roughly 3 working days each, which matches the "first several days of the month are gone" pattern most firms describe.
Automated effort. Extraction itself runs in minutes and isn't the human cost. The human cost is review: 40 statements times ~3 minutes = ~2 hours/month, plus a buffer for correcting flagged exceptions, call it ~6 hours/month total to be safe.
The reduction. 60 hours drops to about 6 hours. That's a ~90% reduction (a conservative read lands at ~85% once you pad the review buffer). The headline isn't a slogan here; it's an arithmetic outcome of stated, adjustable inputs.
Two things matter beyond the hours. First, transcription errors largely disappear because numbers aren't being retyped, and totals are validated on extraction. Second, a multi-day task becomes a same-day one, which changes how the whole month-end feels.
What this means for your firm
The model is only useful if you run it on your own numbers. Take three figures you already know: statements per month, average transactions per statement, and your team's honest keying rate. Multiply the first two for total rows, divide by the rate for monthly hours, then compare that to a few minutes of review per statement. The gap is your potential saving.
Even halve our assumptions and the result is still dramatic, because manual keying scales linearly with volume while review barely moves. That asymmetry is the real story. The reclaimed hours are the obvious win; what you do with them, advisory work, client review, taking on clients without adding headcount, is the one that compounds. If you want to see your own version of these numbers, run a real statement through and compare the structured output to what you'd have typed. The fastest way to trust a model is to test it. Firms weighing this often start with AI data extraction for accountants.