Finance / Reconciliation Live

Bank Transaction Matching

Match bank transactions to ledger entries and leave the genuinely unmatched genuinely unmatched — no forcing, no plugging the difference to make the reconciliation close.

About the Agent

Challenges Bank Transaction Matching addresses

Done by hand, reconciliation means gathering bank statement, ledger entries and matching rules, working through 4 separate passes over the same material, then producing does it reconcile, reconciliation note and unmatched both ways. None of it is difficult and all of it is exacting, which is the combination people are worst at holding. The errors that matter are the ones a tired reader does not notice, and they surface later — in a reconciliation, or in somebody’s reply. It comes round again every weekday at 08:00, whether or not anyone has the time. As volume grows the work does not get harder, only longer, and the first thing to go is the checking.

Bank Transaction Matching runs that same sequence end to end and returns the result as structured artefacts. What it cannot settle it hands over rather than guesses at, and your correction is kept: it asks “Does this reconciliation hold?” after every run, and those answers become the set it is measured against. Nothing that moves money, alters a contract or reaches a customer executes without human approval, and every action is written to an audit log. The gain is in the volume that no longer has to be read, not in removing the judgement.

How it works

Step 1: Reading the bank statement

First of 4. It works from bank statement, ledger entries and matching rules and feeds the step after it.

Key Tasks:

  • Locating the material: It works from bank statement, ledger entries and matching rules, so nothing has to be forwarded, re-keyed or renamed first.
  • Handling the format it arrives in: Scanned pages, native documents, spreadsheets and message bodies are all read the same way, including layouts where the relevant figure sits inside a table rather than a labelled field.
  • Pulling the fields that matter: Only the fields the rest of the run needs are extracted. What cannot be read confidently is recorded as unread rather than filled in with a best guess.

Outcome:

  • Fields extracted: The fields are available to the steps that follow, with anything unreadable listed rather than silently defaulted — which is what stops a bad extraction becoming a confident wrong answer three steps later.

Step 2: Reading the ledger

Step 2 of 4. It takes what step 1 produced and hands its result to step 3.

Key Tasks:

  • Reading the ledger specifically: This pass is scoped to the ledger rather than to the document as a whole, so a field that appears in more than one place is taken from the one that governs.
  • Keeping the original alongside: Each extracted value stays linked to where it was found, so a figure that looks wrong can be checked against the source rather than re-entered.
  • Locating the material: It works from what step 1 produced, so nothing has to be forwarded, re-keyed or renamed first.

Outcome:

  • The ledger captured: The fields are available to the steps that follow, with anything unreadable listed rather than silently defaulted — which is what stops a bad extraction becoming a confident wrong answer three steps later.

Step 3: Matching transactions

Step 3 of 4. It takes what step 2 produced and hands its result to step 4.

Key Tasks:

  • Finding the counterpart: It searches the connected system for the record this one should correspond to, using the identifiers taken from what step 2 produced.
  • Comparing field by field: Each field is checked against its counterpart rather than the documents being compared as wholes, so a single line that disagrees is reported as that line rather than as a failed match.
  • Applying your tolerances: The variance you accept is configuration. A difference inside it clears; a difference outside it is held, and the amount is stated rather than described as a discrepancy.

Outcome:

  • Everything agrees: The record clears and moves on without anyone reading it.
  • Something does not agree: Each disagreeing field is reported with both values and the size of the gap, so the review starts from the discrepancy rather than from the whole document.
  • No counterpart exists: The record is held and flagged as unmatched rather than passed through as clean, which is the failure mode that costs the most to find later.

Step 4: Writing the reconciliation note

Last of 4. It takes what step 3 produced and produces does it reconcile and reconciliation note.

Key Tasks:

  • Aligning the reconciliation note: The comparison is anchored on the reconciliation note, so two records that differ in formatting but agree in substance are not reported as a mismatch.
  • Reporting the gap, not the verdict: Where values differ, both are carried forward with the size of the difference, so the next step decides rather than inherits a decision.
  • Finding the counterpart: It searches the connected system for the record this one should correspond to, using the identifiers taken from what step 3 produced.

Outcome:

  • Aligned: The records correspond within tolerance and the item clears.
  • Out of alignment: The differing fields are carried forward with both values and the size of the gap, for a person to settle.
  • Nothing to align against: The item is held as unmatched rather than passed through as clean, which is the failure mode that costs the most to find later.

Step 5: Your review, and what it changes

The run ends with a person, not with a result being filed.

Key Tasks:

  • Asking a specific question: It asks “Does this reconciliation hold?” rather than for a rating. A question about this run is answerable; a score out of five is not.
  • Keeping the correction: What you change is recorded against the case that produced it, so the disagreement is retrievable rather than absorbed.
  • Building the evaluation set: Those cases become what the agent is measured on. It is scored against your judgement rather than against a general benchmark.

Outcome:

  • A measured agent, not an assumed one: The cases Bank Transaction Matching handles well and the cases it does not are both visible, and the second list is the one that decides what changes. Nothing is retrained silently on the back of a single correction.

Why use Bank Transaction Matching?

  • Checks are evidenced, not asserted: Each check records what was expected and what was found. A failure can be understood — and argued with — without re-running anything.
  • Field mapping you can audit: Every incoming field is shown with what it was mapped to and how confidently. Low-confidence mappings are held rather than applied, which is where silent data corruption otherwise starts.
  • Takes documents as they arrive: Scanned pages, native files and awkward layouts are read as they are. Nothing has to be renamed, re-keyed or converted into a template before a run.
  • Runs without being remembered: It starts every weekday at 08:00, on its own. The work stops depending on whoever used to carry it in their calendar.
  • Corrected by the people using it: After each run it asks “Does this reconciliation hold?”. Those answers become the evaluation set, which means it is measured against your judgement rather than ours.

Oversight

Runs under scoped, least-privilege credentials with every action written to an audit log. Anything that moves money, alters a contract or reaches a customer requires human approval before it executes.

Reconciliation

Other agents in reconciliation

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  • Treasury Management Live

    Capital Expenditure Monitoring

    Track capital projects against their approved case — spend, stage and the benefit that justified them, including the projects quietly costing more than the approval allowed.

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Next Step

Deploy Bank Transaction Matching, or adapt it

It runs as-is. Most deployments diverge — a different source system, a different tolerance, a different approval path. A 30-minute technical call establishes which.

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  • NDA on request
  • Scoping notes sent within 48 hours
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