Portfolio demonstration | GL reconciliation workflow
Bank-to-GL Reconciliation Matcher
Configurable matching, visible exceptions, and an explained bridge for recurring reconciliation work.
A reusable reconciliation model compares bank activity with posted general-ledger entries, applies configurable reference, date, and amount rules, and routes unresolved items into a reviewer-owned exception queue.
Bank and GL exports rarely align row-for-row. Timing differences, duplicated references, missing entries, and small amount differences make manual matching slow and can hide the true cause of the reconciliation gap.
Short demo video using synthetic data. The workflow is illustrative and designed to show the control logic, not a live client system.
What the model demonstrates
- Side-by-side bank and GL ingestion with normalized identifiers.
- Reference, date, and amount-tolerance matching rules.
- Matched-item evidence plus bank-only and GL-only exceptions.
- A reconciliation bridge that explains the remaining difference.
Automation workflow
- Load structured bank and general-ledger exports.
- Normalize signs, dates, references, and account metadata.
- Apply exact-reference and configurable tolerance rules.
- Assign unmatched items to an owner and track review status.
Controls and auditability
- Matched reference and amount-difference tests.
- Bank-vs-GL gross-difference reconciliation bridge.
- Explicit bank-only and GL-only exception counts.
- Model-level PASS status only when the bridge is fully explained.
Workbook deliverables
- Editable matching rules and tolerance assumptions.
- Bank and GL source tables with formula-driven status.
- Detailed match output and review-ready exception queue.
- Control-check sheet for recurring close sign-off.
Reviewer-ready finance automation output
The synthetic demonstration matches 28 of 30 bank transactions to 29 GL entries, producing a 93.3% auto-match rate. Two bank-only items and one GL-only item explain the bridge completely, leaving no unexplained difference.
Important note
This portfolio demonstration uses synthetic data and does not represent a client engagement or guaranteed results. A production build may require duplicate handling, split transactions, settlement-batch logic, FX treatment, fuzzy matching, and system-specific identifiers. Human review remains required for unresolved items.
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