Risk-Constrained Evaluation of Financial Document Recognition: A Formal Framework for Evidence, Critical Errors, and Selective Automation

Abstract:Financial document recognition is commonly evaluated using character and word error rates, which do not adequately represent the consequences of errors in financial workflows. This paper formalizes financial document recognition as a risk-constrained evidence-recovery problem. It defines typed, evidence-linked financial facts and a multi-layer quality model spanning text, layout, tables, fields, semantic relations, calibration, robustness, and operational performance. It introduces non-compensatory acceptance gates based on upper confidence bounds for critical-field errors, and formulates selective automation as a constrained risk-coverage problem with review-capacity limits. The paper also specifies annotation requirements, leakage-resistant benchmark splits, statistical estimands, and a reproducibility card. Formal propositions establish why aggregate scores cannot certify critical-field reliability and why zero observed errors do not imply zero risk. A synthetic example illustrates the decision rule. The work is theoretical; no empirical benchmark or model performance claim is presented.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2611.02775 [cs.CV]

(or arXiv:2611.02775v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2611.02775 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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