Transaction Matching
Transaction matching is the automated process of comparing individual transactions across two or more data sources to identify corresponding entries. In reconciliation, matching engines compare records from banks, payment processors, ERPs, and internal ledgers using configurable rules, fuzzy logic, and machine learning. Modern matching engines achieve 95-99% automatic match rates, leaving only true exceptions for human review.
Key Details
- Match types: one-to-one (single transaction pairs), one-to-many (one payment covers multiple invoices), many-to-many (batch settlements)
- Rule-based matching uses exact or tolerance-based comparisons on amount, date, reference, and counterparty fields
- AI/ML matching handles fuzzy scenarios: slightly different amounts, date offsets, partial references, and name variations
- Confidence scoring ranks match candidates so operators can quickly confirm or reject suggested matches
- Tolerance windows allow matching within configurable thresholds (e.g., amounts within $0.01, dates within 3 days)
- Unmatched transactions route to exception queues with full context from both source and target records
- Match rate is the primary KPI — measured as percentage of transactions automatically matched without human intervention