Intelligent Document Processing and Reconciliation

A financial services team manually extracts information from onboarding documents, statements, invoices, and transaction records. Staff compare values across systems and investigate mismatches. Poor scans and varied layouts increase workload, while errors can affect customers or compliance.

• Document formats and scan quality vary.
• Plausible extracted values can still be wrong, especially account numbers, dates, and amounts.
• Differences may reflect timing, fees, reversals, duplicates, or genuine anomalies.
• Financial data requires controlled access, auditability, and retention policies.

Combine document classification, OCR or layout-aware extraction, deterministic validation, record matching, and a human exception queue. A language model may summarize unstructured information, while exact identifiers, arithmetic, and mandatory checks remain deterministic.

• Classify documents and route them to extraction schemas.
• Extract fields with confidence indicators and source-page references.
• Validate required fields, totals, dates, identifiers, and cross-field consistency.
• Match using exact identifiers first and controlled fuzzy matching when appropriate.
• Categorize timing differences, fees, missing records, duplicates, and unexplained discrepancies.
• Require review for low-confidence results and log versions and reviewer decisions.

Evaluate on a manually reviewed sample spanning document types and quality levels. Measure field-level exact-match accuracy, false-match rate, missed-discrepancy rate, processing time, and manual-review percentage. Test duplicate submissions, missing pages, conflicting totals, and malformed content; begin read-only.

• Processing time per document or batch.
• Extraction accuracy by document type.
• False-match and missed-discrepancy rates.
• Manual intervention rate and audit investigation time.