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

Yichao Jin; Yushuo Wang; Yuxuan Han; Kwan Ching Yee Sonia; Weiyang Song; Chiu Jin-Chun Kent; Wong Chong Hwee; Wong Tiong Kiat; Kenneth Zhu Ke; Jingyuan Zhao

arXiv · 2609.20110

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

Yichao Jin·Yushuo Wang·Yuxuan Han·Kwan Ching Yee Sonia·Weiyang Song·Chiu Jin-Chun Kent·Wong Chong Hwee·Wong Tiong Kiat·Kenneth Zhu Ke·Jingyuan Zhao

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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.

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Yichao Jin, Yushuo Wang, Yuxuan Han, Kwan Ching Yee Sonia, Weiyang Song, Chiu Jin-Chun Kent, Wong Chong Hwee, Wong Tiong Kiat, Kenneth Zhu Ke, Jingyuan Zhao. 2026-09-17. Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents. https://arxiv.org/abs/2609.20110

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