Financial document recognition assessed with risk limits and error focus
Risk-Constrained Evaluation of Financial Document Recognition: A Formal Framework for Evidence, Critical Errors, and Selective Automation
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Current ways to check how well financial document systems work focus on counting mistakes, but they miss how costly some errors can be in real finance tasks. The authors treat the problem as one of carefully recovering correct facts while controlling risks of important mistakes. They create a detailed quality model covering text and data structures and design checks that don't let risky errors pass. Their math shows why just counting errors isn't enough to guarantee safety for critical data. This paper is theoretical and does not test any real-world system.
What this means in practice
- •For financial compliance teams: Set strict error limits for automated financial data extraction to avoid costly mistakes in audits and reporting processes.
- •For enterprise automation engineers: Design review thresholds in document automation workflows that guarantee control over critical error risks within capacity limits.
A theory result. No direct application yet.
Authors
Subodh Lonkar
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.