I've Done Hundreds of Bank Reconciliations. Most of the Work Shouldn't Exist.

The worst bank reconciliation I ever did was for a trading company in Singapore with twelve bank accounts across four currencies. It was March close, the busiest month. I sat in a windowless conference room with two monitors, three browser tabs open (DBS, OCBC, and HSBC), an Excel workbook with seventeen tabs, and a growing sense of dread.

It took me four days. By the end, I'd matched about 1,800 transactions, investigated 40-odd discrepancies (most of which turned out to be timing differences or bank fees I'd missed), and made a dozen adjusting entries. My eyes hurt. My back hurt. And I knew I'd be doing it all again in four weeks.

That was eight years ago. I've since done this for companies across Singapore, Dubai, Sydney, and London, and the basic shape of the problem hasn't changed much, even with better tools. The reason, I think, is that most reconciliation software automates the easy part and leaves you alone with the hard part.

The 80/20 that nobody talks about

Here's what I've observed across dozens of month-end closes: roughly 80% of bank transactions match cleanly to internal records. Same amount, same date (give or take a day), same counterparty. A decent matching rule in Xero or NetSuite handles these fine.

The remaining 20% is where all the time goes. And it's always the same handful of problems:

Partial matches. A customer pays three invoices with a single bank transfer. The bank shows one deposit for $47,250. Your AR shows three separate invoices for $15,000, $18,750, and $13,500. A human can figure this out. Most matching engines can't, or they flag it as three separate exceptions.

Bank fees and adjustments. You sent a $10,000 wire transfer. The bank charged a $35 fee and a $12 FX conversion charge. The bank statement shows $10,047 out. Your books show $10,000. That $47 difference creates a reconciling item that someone has to investigate, identify as fees, and book to the right expense account.

Description mismatches. The bank says "PYMNT FROM ACME CORP REF:INV-2847." Your system lists "Acme Corporation - Invoice #2847." A human sees these are the same thing instantly. Traditional matching rules built on exact string comparison don't.

Timing differences across month boundaries. A check issued on the 28th clears on the 3rd of the next month. An ACH payment initiated on the 30th settles on the 1st. These create reconciling items that are perfectly normal but still require someone to verify and document.

What would actually help

Based on years of doing this, here's what I think a genuinely useful reconciliation tool would do differently:

It would handle one-to-many and many-to-one matching natively, not as an exception. If three AR invoices add up to one bank deposit from the same customer, that should auto-match, not generate three separate flags.

It would know about bank fees. If the difference between the expected amount and the actual amount matches a known fee pattern for that bank, it should auto-categorize and suggest the journal entry.

It would use fuzzy matching on descriptions. Not just exact string comparison, but understanding that "ACME CORP" and "Acme Corporation" are the same entity, and that "REF:INV-2847" maps to "Invoice #2847."

And it would reconcile continuously, not monthly. Industry surveys consistently show that about 50% of finance teams still take six or more business days to complete their month-end close, with cash reconciliation frequently cited as the biggest bottleneck. If matching happened daily, the month-end close would be a review, not a marathon.

What we're designing at ScribeArc

We're building our reconciliation engine around these specific pain points. The approach is to parse uploaded bank statements from any format (PDF, CSV, MT940 - we're starting with the formats most common in APAC and EMEA), apply AI matching that handles the partial, fuzzy, and timing cases natively, and surface only the genuinely ambiguous items for human review with a suggested resolution.

We're testing this in private beta right now. I don't have measured match rates to publish - we're pre-launch and it would be dishonest to claim numbers we haven't validated at scale. What I can say is that the design is informed by years of doing this work the hard way, and we're optimizing for reducing the 20% of transactions that eat 80% of the time.

My honest belief: the technology to make bank reconciliation a same-day exercise for most companies exists today. The reason it hasn't happened yet is that most tools were built to automate the easy matches and left the hard ones as "exceptions." We're trying to go after the exceptions directly.

ScribeArc is in private beta. Numbers cited about our product are targets, not measured results.