Safe Invoice Data Extraction: FPR-Constrained Validation with VLM Denoising

Abstract

Automating invoice processing in enterprise settings requires more than high extraction accuracy: it demands a reliable mechanism to decide when an extraction is trustworthy enough to bypass human review. While modern Vision-Language Models (VLMs) achieve state-of-the-art results on document benchmarks, their integration into production pipelines remains hindered by the lack of principled methods to guarantee a bounded False Positive Rate (FPR). In financial workflows, an undetected extraction error (False Positive in production) can trigger irreversible erroneous payments, whereas a False Negative only incurs a reversible human review cost. In this paper, we formalize autonomous invoice validation as a selective classification problem under a field-level FPR constraint. We propose a multi-stage architecture that combines Optical Character Recognition (OCR) and Natural Language Processing (NLP) for field extraction, rule-based rejection, VLM-based correction, and a tabular Machine Learning (ML) scoring module. A key finding is that the VLM's most significant contribution is as an extraction denoiser: by correcting systematic OCR-NLP errors on fields not guarded by business rules, it significantly increases coverage (defined as documents not requiring human review) under a strict 1% field-level FPR target, while also bringing the FPR from above the target to below 1%. Furthermore, we apply a geometric safety layer, providing a tunable “safety knob” that ensures robust FPR guarantees across data folds at a quantifiable coverage cost. Evaluated on 11,253 real-world enterprise invoices, our system achieves 73% auto-validation coverage at a 0.96% field-level FPR, outperforming confidence-based baselines. Our results demonstrate that safe document automation requires not only high extraction accuracy but equally principled risk-constrained validation and the mitigation of label noise in production data.