Transaction Matching
Related terms
Category
Financial Reporting
Transaction matching is the process of pairing individual transactions from two or more data sources to verify that each entry has a corresponding counterpart. It is a core component of account reconciliation, used extensively for bank reconciliations, intercompany reconciliations, and sub-ledger-to-GL comparisons.
Why it matters
Many balance sheet accounts cannot be reconciled by simply comparing two balances. Instead, accountants must match individual transactions line by line to verify that every entry in one system has a corresponding entry in the other. Bank reconciliations are the classic example: each deposit and withdrawal in the GL must be matched to a corresponding item on the bank statement, with unmatched items identified as reconciling items such as outstanding checks or deposits in transit.
Manual transaction matching is among the most tedious tasks in accounting. A single bank account may have thousands of transactions per month, and differences in posting dates, amounts, descriptions, and reference numbers make exact matching difficult. Accountants often spend hours scrolling through spreadsheets, visually scanning for matches, and manually linking rows. The process scales poorly: doubling transaction volume more than doubles the time required because the number of possible pairings grows exponentially.
Automated transaction matching uses rules and algorithms to pair transactions based on configurable criteria such as amount, date range, reference number, and description similarity. This transforms a multi-hour manual task into a process that completes in minutes, with human attention needed only for the unmatched exceptions.
Automated transaction matching engines handle the volume and complexity that make manual matching impractical, applying configurable rules across millions of transactions.
How Arvexi handles this
Arvexi's Transaction Matching engine supports one-to-one, one-to-many, and many-to-many matching across unlimited data sources. The platform ingests data from ERPs, banks, sub-ledgers, and flat files through its 7 data integration tools, then applies configurable matching rules that account for timing differences, partial matches, and description variations.
Unmatched transactions are surfaced as reconciling items with suggested matches ranked by Cortex's confidence analysis. Preparers can accept suggested matches, manually pair transactions, or flag items for investigation. The matching history is preserved as part of the reconciliation work paper, providing a complete audit trail of how each transaction was resolved.