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New pipeline improves AI extraction accuracy for long financial documents

Researchers have developed a multistage extraction framework designed to improve the accuracy of extracting structured information from long, scanned financial documents. This pipeline integrates image preprocessing, OCR, page-level retrieval, and vision-language model (VLM) based extraction, separating page localization from multimodal reasoning. Tested on 120 production KYC documents, the framework demonstrated significant improvements, with the best configuration achieving 87.27 percent accuracy, outperforming direct VLM application by up to 31.9 percentage points. AI

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IMPACT Enhances structured data extraction from complex financial documents, potentially streamlining compliance and KYC workflows.

RANK_REASON Academic paper detailing a new framework for information extraction from financial documents.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Yuxuan Han, Yuanxing Zhang, Yushuo Wang, Yichao Jin, Kenneth Zhu Ke, Jingyuan Zhao ·

    A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows

    arXiv:2604.26462v1 Announce Type: new Abstract: Structured information extraction from long, multilingual scanned financial documents is a core requirement in industrial KYC and compliance workflows. These documents are typically non machine readable, noisy, and visually heteroge…

  2. arXiv cs.CV TIER_1 · Jingyuan Zhao ·

    A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows

    Structured information extraction from long, multilingual scanned financial documents is a core requirement in industrial KYC and compliance workflows. These documents are typically non machine readable, noisy, and visually heterogeneous. They usually span dozens of pages while c…