Reconciliation is the quiet backbone of trustworthy financial reporting. Get it wrong — or worse, skip it — and every number downstream becomes suspect. As a fractional CFO, the reconciliations I see are rarely "bank balance vs. ledger." They span multiple bank accounts, payment processors, AR/AP subledgers, accruals, intercompany transfers, and revenue recognition schedules. Here's how I use matchdata.ai to tame that complexity.
Why traditional reconciliation breaks down
Classic reconciliation assumes a clean one-to-one match: a bank deposit maps to an invoice, a credit card charge maps to an expense. In growth-stage companies, that assumption collapses fast:
- Many-to-many matches — a single customer payment covers three invoices minus a credit; one wire funds two properties.
- Cross-system timing — Stripe settles in batches a day late; the ERP posts the same day.
- Reference data gaps — bank statements reference PO numbers the ledger doesn't store, or vice versa.
Spreadsheets hit a wall here. VLOOKUPs can't reason about "these five lines net to that one line."
How matchdata.ai handles it
matchdata.ai is built around probabilistic matching across heterogeneous data sources. The workflow I use:
- Ingest — Upload or connect the two (or more) datasets: a bank statement CSV, a GL export, a Stripe payout report. matchdata.ai normalizes columns, dates, and amounts automatically.
- Define match rules — I set tolerances (e.g., ±$0.01 for rounding, ±2 days for settlement lag) and keys (invoice number, customer ID, reference text).
- Run fuzzy + grouped matching — The engine proposes one-to-one, one-to-many, and many-to-many groupings, surfacing the evidence behind each proposal.
- Review exceptions — Unmatched or low-confidence items land in a queue I can clear in minutes, not days.
A real example
A client had 1,400 unmatched transactions between their bank and NetSuite — a backlog spanning four months. Manual cleanup was estimated at two weeks. With matchdata.ai, I configured match rules on amount, date window, and a fuzzy reference match on memo text. The first pass cleared 1,180 items automatically. A second pass with relaxed date tolerance cleared another 140. The remaining 80 were genuine exceptions (duplicate wires, a misclassified refund) that took an afternoon to resolve.
Why it matters for the CFO
Reconciliation isn't busywork — it's the audit trail that underwrites every metric you report to your board. matchdata.ai turns it from a monthly fire drill into a repeatable, evidence-backed process you can defend to auditors and investors alike.
If you're spending more than a day a month on reconciliation, let's talk.