Data matching refers to the process of comparing and aligning information coming from multiple sources to identify correspondences, discrepancies or missing elements. In finance, it is a foundational capability: invoices must match purchase orders, payments must align with bank statements, and supplier records must reconcile with ERP data. Without robust data matching, teams face duplicated entries, undetected errors and time-consuming manual checks.
Traditional finance systems perform basic matching based on static rules, amount, date, reference number. However, real-world data is rarely this clean. Formats vary, metadata is incomplete, and exceptions are the norm. This is where autonomous AI becomes transformational. Instead of applying rigid logic, an AI agent can interpret documents, understand relationships between records and adjust to context, making data matching far more accurate and resilient.
Phacet operationalises this shift by embedding data matching capabilities at the core of its agents. They analyse multiple data streams, bank feeds, invoices, contracts, delivery notes, ERP transactions—and map them intelligently, even when information is imperfect or unstructured. Agents detect duplicates, surface anomalies and proactively flag mismatches before they propagate downstream. Every action is logged, auditable and aligned with internal controls.
This intelligent form of data matching enables finance teams to eliminate the bottlenecks traditionally caused by fragmented systems and manual reconciliation. It reduces operational risk, strengthens reporting accuracy and frees teams to focus on exception handling rather than repetitive checks.
The impact becomes especially clear when teams need to reconcile bank transactions at scale. Phacet’s agents handle the complexity automatically, ensuring that unmatched flows are detected early and that financial records remain consistently aligned across systems.
