金融文档识别的人本化风险约束评估框架：证据、关键错误与选择性自动化
Human-Centred, Risk-Constrained Evaluation of Financial Document Recognition: Evidence, Critical Errors, and Selective Automation
AI 导读
arXiv 论文提出一个人本化、风险约束的金融文档识别评估框架，把识别视为证据恢复与决策支持，而非仅用字符或词错误率衡量。框架定义与可见来源区域关联的类型化金融事实，设置非补偿性验收门槛以防总量分数掩盖金额、标识符、正负号等关键字段错误，并将选择性自动化建模为兼顾残余风险、人工复核容量与成本的受限路由问题。论文属理论与方法贡献，未提出新模型、数据集或实证结果。
正文 · 原文
Abstract:Financial document recognition is often assessed using character and word error rates, although these metrics do not express whether a system preserves the financial facts on which people and organisations act. This paper develops a human-centred, risk-constrained framework for evaluating financial document recognition as evidence recovery and decision support. It defines typed financial facts linked to visible source regions and separates verbatim transcription, deterministic normalization, and business interpretation. A layered quality model covers text, localization, layout, tables, fields, fact correctness, evidence grounding, uncertainty, robustness, and operational performance. Non-compensatory acceptance gates use one-sided uncertainty bounds for critical-field errors, preventing strong aggregate scores from masking failures in amounts, identifiers, signs, periods, or entity associations. Selective automation is formulated as a constrained routing problem that accounts for residual risk, human-review capacity, and review cost. The framework further specifies annotation and adjudication procedures, leakage-resistant benchmark partitions, statistical estimands, subgroup analyses, and a reproducibility card. Formal results establish the limitations of compensatory scoring, the non-zero upper risk bound after zero observed errors, and a loss sensitive decision rule for routing uncertain cases to human review. The contribution is theoretical and methodological; no new model, dataset, or empirical performance result is claimed.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.02775 [cs.CV] |
| (or arXiv:2611.02775v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.02775 arXiv-issued DOI via DataCite |
Submission history
From: Subodh Lonkar [view email]
[v1]
Mon, 2 Nov 2026 08:51:01 UTC (23 KB)
[v2]
Thu, 5 Nov 2026 10:23:18 UTC (595 KB)
来源：arXiv cs.AI · arxiv.org