金融文档处理新方法提升关键信息提取准确率
Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents
这个方法挺实用的，专门针对金融文档处理，能自动批准更多字段，减少人工审核，提升效率。
这篇论文提出了一种新的方法，通过分解置信度信号为感知、布局和验证三个可解释的通道，来提升从金融文档中提取关键信息的准确率。该方法在真实发票、合成发票和广告购买表单等三个公共数据集上进行了验证，使用的是Qwen3.6-27B和Gemini-3.1-Flash-Lite两个不同的大语言模型。实验结果显示，该方法将准确率从0.54-0.74提升到了0.90-0.99，显著提高了正确提取与错误提取的区分度。更重要的是，该方法在工业部署中表现良好，能够自动批准49-72%的字段，同时将接受层的误差控制在目标误差以下。
Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents
Straight-through processing (STP) on extracted key-value fields from financial documents without human review requires a calibrated probability together with a bounded guarantee on the residual error of the auto-approved tier. The emergence of modern Vision Language Models (VLMs) provides an out-of-the-box capability for extracting the key-values, but their verbalized confidence signals are unreliable and weakly track field correctness. This paper introduces a decomposed confidence layer along three interpretable channels, including perception, layout, and validation. Together with a final conformal risk control, the score can be used for reliable STP of financial documents. The method is validated on three public datasets covering real invoices, synthetic invoices, and ad-buy forms, using two different VLM families (Qwen3.6-27B and Gemini-3.1-Flash-Lite). Our decomposed score consistently improves the separation of correct from incorrect extractions, substantially raising the AUROC from 0.54-0.74 for VLM verbalized signals to 0.90-0.99 with contributions from all three designed channels. Crucially for industrial deployment, this enables usable STP. The native VLM confidence signals could clear only 0.1%-7.0% of fields under risk control at a target error of <10%. In contrast, the proposed method auto-approves 49-72% of fields while holding the empirical error of the accepted tier at or below the target.
