Reconcile Your Bank and Ledger Automatically
AI matches every bank statement line to the right ledger entry on amount, date, reference and pattern. It clears the easy matches on its own, groups the likely ones for a quick confirm, and flags the real exceptions for a person. Reconciliation stops being a monthly slog.
50+ ERP and software projects. Zero failed projects since 2021.
Line-by-line reconciliation eats a week you do not have
The work is repetitive, the volume is high, and one wrong match hides a real problem. This is exactly the kind of task AI handles well, with a person on the exceptions.
Most lines match on their own
The bulk of every statement is routine: a payment in, a receipt out, a transfer that lines up cleanly with a ledger entry. The AI clears those confident matches without asking, so your team never opens them again.
- Confident matches cleared automatically.
- Likely matches grouped for a quick confirm.
- A short queue instead of a full statement.
The problems surface instead of hiding
A missing entry, a duplicate, a bank charge with no record, an amount that is off by a little: these are the lines that matter. The AI pulls them out and labels them, so a person looks only at the cases that need judgement.
- Only the true exceptions reach a person.
- Each one is labelled with why it was flagged.
- Errors get found early, not at year end.
It reads more than the amount
For every bank line, the AI weighs four signals against your ledger. When they line up, the pair clears. When only some line up, it becomes a quick confirm. When they do not, it is flagged.
From raw statement to a reconciled ledger
The cases that slow a manual reconciliation down
These are the everyday situations the AI is built to sort, so your team is not the one untangling them by hand.
One-to-one matches
A single bank line against a single ledger entry, matched on amount, date and reference and cleared automatically.
Split and grouped payments
One bank credit that settles several invoices, or several receipts that make up one deposit, grouped and reconciled together.
Messy references
Short bank narrations, missing invoice numbers and inconsistent payer names, read on pattern rather than an exact string.
Fees and charges
Bank fees, interest and small deductions that never had a ledger entry, flagged so they can be posted correctly.
Duplicates and reversals
Repeated credits, reversed transfers and double postings caught before they quietly break the balance.
Timing differences
Entries that hit the bank and the ledger on different days, matched across the gap instead of showing up as a mismatch.
What automating reconciliation gives back
Why teams choose TCB Infotech for finance automation
Bank reconciliation automation questions
What is AI bank reconciliation?
How does the AI decide what is a match?
Does a person still review anything?
Which model does it use?
Does it work with our accounting or ERP system?
How long does it take to set up?
See Your Own Statement Reconciled by AI
Book a free consultation. Bring a recent statement and your ledger, and we will show you how much of it the AI would match on its own and what would reach a person.
Book a Free Consultation →No commitment. A practical first look at your reconciliation.
What Is Inside AI Bank Reconciliation
The service is a working part of your close, not a report. Here is what it matches on, what it clears on its own, what it hands your team, and how it writes back to your ledger.
From a raw statement to a reconciled ledger
It matches the way a good accountant would
Every bank line is weighed against your ledger on amount, value date, reference or narration, and the pattern of how a payer usually appears. A match is not a single exact string, so short bank descriptions and inconsistent names still find their entry.
- Amount, date, reference and pattern weighed together.
- Reads messy narrations instead of failing on them.
- Tuned on your own history, not a generic rule set.
The easy volume clears, the rest is a quick confirm
Confident matches are cleared without anyone opening them. Likely matches are grouped into a short confirm queue, so your team approves the ones that look right in a couple of clicks instead of hunting for them across the statement.
- High-confidence pairs cleared automatically.
- Likely matches grouped for a fast confirm.
- A short queue in place of a full statement.
Only the real problems reach a person
Anything that does not fit is labelled with why it was flagged: a missing entry, a duplicate, an unmatched fee, an amount that is off. A person resolves those, and every cleared and confirmed match is written back to your ledger so the books stay in one place.
- Exceptions labelled with the reason.
- A person owns the cases that need judgement.
- Matched entries written back to your ledger.
Everything the service delivers
Four-signal matching
Amount, value date, reference and payer pattern weighed together for every bank line.
Automatic clearing
Confident matches cleared on their own, so no one reopens the routine lines.
Confirm queue
Likely matches grouped for a quick approval, ranked so the closest sit on top.
Exception list
Only the real problems, each labelled with why it was flagged for a person.
Ledger write-back
Cleared and confirmed matches posted back to your ERP or accounting ledger.
Model-agnostic build
Runs on the models that suit your accuracy and hosting rules, swappable later.