Glossary

Data reconciliation logic refers to the set of rules and matching mechanisms used to compare, align, and validate data coming from different financial sources. Its purpose is to determine whether two or more data points actually represent the same transaction, balance, or business event.

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In finance operations, reconciliation logic is critical because data rarely originates from a single system. Invoices, bank transactions, ERP entries, and operational records often follow different formats, timings, and identifiers. Without robust reconciliation logic, finance teams are forced to rely on manual checks or assumptions, which increases error rates and delays decision-making.

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Effective data reconciliation logic goes beyond simple exact matching. It incorporates tolerances, contextual rules, historical patterns, and business constraints to identify true matches, partial matches, and anomalies. This allows compliant data to flow through automatically, while discrepancies are flagged for investigation.

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Modern finance platforms increasingly rely on AI agents to apply reconciliation logic at scale and in real time. At Phacet, these mechanisms are central to processes such as bank transaction reconciliation, where accurate matching is a prerequisite for reliable reporting and decision-making.