Automated Bank Reconciliation
Bank reconciliation is treated as routine bookkeeping, but the actual work is tedious pattern-matching between a bank feed and your ledger — a batch deposit that represents six separate customer payments, a bank fee that never got booked, a transaction that cleared on a different date than it posted. Most accounting software auto-matches the easy 80% and leaves a residual list of unmatched items that someone has to manually trace, transaction by transaction, every week or month. Left undone even briefly, this residual list grows, and month-end close gets delayed while someone works through weeks of backlog at once.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 5-8 hrs/week for a mid-sized finance team.
How the automation works
We build a reconciliation layer that goes beyond simple one-to-one transaction matching, decomposing batch deposits into their component customer or vendor payments, tolerance-banding small FX and rounding differences instead of flagging every cent variance, and learning your recurring transaction patterns (the same monthly bank fee, the same payroll provider debit) so they match automatically after the first occurrence. Only genuine exceptions — a transaction with no plausible ledger counterpart, an amount mismatch outside tolerance — reach a human, with the likely explanation already suggested rather than a bare unmatched line.
Process flow
- 01
Bank feed syncs trigger
Transactions flow in automatically from your connected bank feed, no manual statement download or CSV import required.
- 02
Decompose batch transactions ai
Batch deposits and bulk debits are broken into their component transactions and matched individually against the ledger, not treated as one unmatchable lump.
- 03
Match with tolerance ai
Transactions are matched against ledger entries with configurable tolerance for FX rounding and timing differences, rather than requiring exact cent-for-cent matches.
- 04
Learn recurring patterns ai
Recurring transactions like bank fees or payroll debits are recognised after the first occurrence and matched automatically on subsequent statements.
- 05
Surface genuine exceptions output
Only transactions with no plausible match are flagged, each with a suggested likely explanation rather than a bare unmatched line for someone to investigate cold.
- 06
Post reconciled status integration
Matched transactions are marked reconciled directly in your accounting system, keeping the ledger continuously current instead of caught up in a periodic batch.
Inputs
- Bank transaction feed
- General ledger transaction history
- Known recurring transaction patterns
- FX rate source for multi-currency accounts
Outputs
- Reconciled bank transactions
- Genuine exception queue with suggested explanations
- Reconciliation completeness report
- Unreconciled aging by transaction age
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Batch deposits and payroll bulk debits break naive one-to-one matching entirely — the reconciliation needs to decompose a single bank line into its component ledger entries, or every batch transaction lands in the exception queue regardless of how routine it actually is.
- FX rounding creates persistent cent-level mismatches on multi-currency accounts that must be tolerance-banded, not force-matched or flagged individually — a fixed small tolerance appropriate to your transaction volume avoids both false exceptions and silently accepting real discrepancies.
- Never auto-post a match below your confidence threshold purely to clear the exception queue faster — an incorrectly matched transaction that gets marked reconciled is far more expensive to unwind during an audit than one sitting unmatched for review.
- A transaction that clears the bank on a different date than it was recorded in the ledger (a check that took two weeks to cash) is not a real discrepancy — the matching window needs enough date flexibility to account for normal clearing lag, or you'll generate exceptions for perfectly ordinary timing differences every single period.
Frequently asked questions
How accurate is automated bank reconciliation?
Once tuned to your transaction patterns, match rates of 90%+ on routine transactions are typical, with the remainder routed as genuine exceptions for review rather than force-matched or ignored.
Does this handle batch deposits from a payment processor correctly?
Yes — this is one of the most common reconciliation failure points, and the system decomposes batch deposits into their component transactions rather than leaving the whole batch unmatched.
What happens with multi-currency bank accounts?
FX conversion differences are tolerance-banded per your configured threshold, so routine rounding doesn't generate false exceptions while genuine mismatches still get flagged.
How is this different from the auto-matching already built into our accounting software?
Native auto-match handles simple exact matches well but typically leaves batch transactions, tolerance-eligible variances and recurring patterns in the manual queue — this layer extends matching logic specifically to close that gap.
How current does our bank feed need to be for this to work well?
A daily-syncing bank feed is ideal; the reconciliation runs on whatever cadence your feed updates, and more frequent syncing means exceptions surface sooner rather than piling up.