Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents

2026-09-17

AI Summary

本文提出一种分解置信层方法，通过感知、布局和验证三个通道提高金融文档直通处理的可靠性，并在多个数据集上验证了其有效性。

Abstract

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.

Problem

• Straight-through processing
• Financial documents
• Calibrated probability
• Residual error
• Vision Language Models

Innovation

• Decomposed Confidence Layer
• Straight-Through Processing (STP)
• Vision Language Models (VLMs)
• Conformal Risk Control