LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing

2026-08-17

AI Summary

This study addresses verification challenges in financial document auditing arising from heterogeneous formats and embedded rules by proposing a logic-aware enhancement framework based on multimodal large language models. The framework employs a four-stage modular pipeline integrating document retrieval, layout-preserving extraction, metadata augmentation, and symbolic verification to enable fine-grained error attribution and end-to-end traceability. Experiments on real-world benchmarks demonstrate that this approach significantly outperforms baselines by effectively mitigating hallucinations and accurately handling edge cases while maintaining efficient token utilization. Consequently, the proposed method satisfies the stringent requirements for high trustworthiness and interpretability in high-frequency auditing scenarios, offering a robust solution for complex financial compliance tasks.

Abstract

Financial document validation in production, such as payroll auditing, tax compliance, and loan underwriting, demands exceptional accuracy, consistency, and reproducibility under strict enterprise constraints. In practice, documents arrive with heterogeneous layouts and formats, semantically rich and context-dependent content, and embedded business rules that current pipelines struggle to process reliably. We introduce LAVA (Logic-Aware Validation and Augmentation), a modular, backbone-agnostic pipeline built on multimodal large language models, that integrates a four-stage design: document-rule retrieval, layout-preserving information extraction, auxiliary metadata enrichment, and auditable symbolic/arithmetic verification. LAVA supports robust rule grounding, fine-grained error attribution, and consistent, traceable end-to-end execution, capabilities essential for high-stakes deployment. Evaluated on a large real-world benchmark with diverse financial documents and dozens of expert-curated validation rules, LAVA outperforms baselines in hallucination control and edge-case handling while maintaining efficient token usage, demonstrating practicality for high-volume, time-critical validation.

Problem

• Financial Document Auditing
• Document Validation
• Heterogeneous Layouts
• Business Rules
• Information Extraction

Innovation

• Logic-Aware Validation
• Multimodal LLMs
• Symbolic Verification
• Layout-Preserving Extraction
• Financial Document Auditing