Why an LLM Alone Cannot Do Invoice Extraction Yet

Описание и применение

LLM может читать счета без шаблонов, но без проверки, сопоставления и политики автоматизация не завершена. Комбинируя LLM с детерминированными проверками и ручным обзором, достигается более высокая точность и аудитируемость.

Задача

Invoice extraction systems that rely solely on large language models (LLMs) fail to fully automate accounts payable because they lack validation, matching, policy enforcement, and exception handling.

Результат

Combining LLM reading with deterministic post‑processing and human review significantly improves straight‑through processing rates and audit readiness, while keeping operational costs manageable.

Рабочий процесс

• 1. Submit invoice to multimodal LLM for template‑free extraction into JSON.
• 2. Run arithmetic validation: check line‑item totals, subtotal, tax, and overall total.
• 3. Perform entity resolution: match vendor name and purchase order to internal master data using fuzzy matching and thresholds.
• 4. Apply policy checks: approval thresholds, tax treatment, cost centre allocation, and duplicate detection across history.
• 5. Route invoices to exception queue if any step fails or confidence is below field‑specific thresholds.
• 6. Human reviewers correct errors, creating labeled examples for future model improvement.
• 7. Store validated data in the accounting system.

Доказательства

Template‑free extraction is the thing that broke. Rule‑based and template‑based systems required per‑vendor configuration...

Источник

Step two is validation. Do the line items sum to the subtotal...

Источник

The most reliable way to see this distinction in practice is to score a model on documents rather than on fields...

Источник