Large-scale reconciliation between internal data (ERP) and external supporting documents, when dependent on human review, accumulates fatigue that causes critical discrepancies to be missed. Upstage Studio extracts key fields from documents—export declarations, invoices, B/Ls—and structures them for comparison against system data. This creates a reliable, auditable data layer that supports verification workflows.

Sophisticated document forgeries and abnormal transaction patterns are difficult to detect through manual review alone. When an LLM node is connected, Upstage can cross-reference extracted fields against historical records to flag patterns that deviate from expected norms—impersonation attempts, duplicate submissions, and amount discrepancies. Teams gain an earlier signal to investigate before losses occur.

Reactive, post-audit-focused approaches only recognize problems after losses have already occurred. With an LLM node connected, Upstage can generate a concise anomaly report specifying the location and reason for each flagged item, giving the responsible party a clear starting point for follow-up. Earlier visibility into discrepancies reduces the window for financial incidents to escalate.