FinRule-Bench: A Benchmark for Joint Reasoning over Financial Tables and Principles
Arun Vignesh Malarkkan, Manan Roy Choudhury, Guangwei Zhang, Vivek Gupta, Qingyun Wang, Yanjie Fu, and Denghui Zhang

TL;DR
FinRule-Bench is a new benchmark designed to evaluate large language models' ability to perform rule-based reasoning and verification on real-world financial statements, highlighting their strengths and limitations in high-stakes financial analysis.
Contribution
The paper introduces FinRule-Bench, a comprehensive benchmark with tasks for verifying, identifying, and diagnosing rule violations in financial statements, using real data and explicit principles.
Findings
Models excel at simple rule verification.
Performance drops significantly on complex rule discrimination.
Multi-violation detection remains challenging for LLMs.
Abstract
Large language models (LLMs) are increasingly applied to financial analysis, yet their ability to audit structured financial statements under explicit accounting principles remains poorly explored. Existing benchmarks primarily evaluate question answering, numerical reasoning, or anomaly detection on synthetically corrupted data, making it unclear whether models can reliably verify or localize rule compliance on correct financial statements. We introduce FinRule-Bench, a benchmark for evaluating diagnostic completeness in rule-based financial reasoning over real-world financial tables. FinRule-Bench pairs ground-truth financial statements with explicit, human-curated accounting principles and spans four canonical statement types: Balance Sheets, Cash Flow Statements, Income Statements, and Statements of Equity. The benchmark defines three auditing tasks that require progressively…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Auditing, Earnings Management, Governance · Financial Reporting and XBRL
