FinanceComplexQA: Benchmarking Agentic Reasoning on Industrial-grade Financial Documents

Analysis

Why This Paper Matters

This paper addresses a critical gap in evaluating agentic reasoning for real-world financial document analysis. Existing benchmarks often rely on simplified or synthetic documents that do not capture the complexity of industrial-grade financial reports, which feature intricate layouts, dense numerical data, and multi-hop reasoning requirements. By introducing FinanceComplexQA, the authors provide a realistic and challenging testbed that closely mirrors the tasks faced by financial analysts. The benchmark's bilingual support and coverage of six mainstream scenarios make it particularly relevant for global financial applications.

The significance is amplified by the growing adoption of RAG systems and agentic reasoning tools in finance. Without rigorous benchmarks like FinanceComplexQA, practitioners lack reliable methods to compare systems and identify weaknesses. The paper's focus on failure case analysis—especially in numerical computation and multi-hop reasoning—offers actionable insights for improving model architectures and training strategies.

Technical Contributions

• Finance-LaTeX SKILL: A novel skill for synthesizing financial documents with complex layouts, leveraging expert knowledge to ensure realism and diversity.
• Agent Workflow for Data Generation: An automated pipeline that produces 2,000 professional documents and 6,000 high-quality QA pairs, reducing manual annotation effort.
• FinanceComplexQA Benchmark: Contains 2,026 deep research tasks across 1,009 documents, with eight key features including bilingual support, seven task types, and stable reference answers.
• Agent-as-a-Judge Evaluation: A precise evaluation framework using multiple metrics to assess agent performance, enabling fine-grained comparison.
• Comprehensive Evaluation: Systematic testing of leading RAG systems and agentic reasoning tools, with detailed failure case analysis across four dimensions: numerical computation, multi-hop reasoning, content summarization, and industry analysis.

Results

The paper reports a comprehensive evaluation of state-of-the-art RAG systems and agentic reasoning tools on FinanceComplexQA. While specific numerical metrics are not detailed in the abstract, the evaluation identifies significant performance variation among different agents. Failure case analysis reveals common weaknesses: numerical computation errors in financial calculations, breakdowns in multi-hop reasoning chains, inadequate content summarization of lengthy documents, and insufficient domain knowledge for industry-specific analysis. These findings provide a roadmap for targeted improvements in agentic reasoning systems.

Significance

FinanceComplexQA sets a new standard for evaluating agentic reasoning in financial document analysis. By providing a realistic, challenging, and reproducible benchmark, it enables fair comparison of RAG systems and agentic tools, accelerating progress in the field. The benchmark's design principles—bilingual support, complex layouts, expert-level questions—can be adapted to other domains such as legal or scientific document analysis. The failure case analysis offers concrete guidance for researchers and practitioners aiming to improve numerical reasoning, multi-hop inference, and domain adaptation in AI agents. This work is likely to influence both academic research and industrial deployment of financial AI systems.