BizFinBench: A Business-Driven Real-World Financial Benchmark for
  Evaluating LLMs
Authors: name: Guilong Lu; name: Xuntao Guo; name: Rongjunchen Zhang; name: Wenqiao Zhu; name: Ji Liu
Published: 2025-05-26 03:23:02

Abstract: Large language models excel in general tasks, yet assessing their reliability
in logic-heavy, precision-critical domains like finance, law, and healthcare
remains challenging. To address this, we introduce BizFinBench, the first
benchmark specifically designed to evaluate LLMs in real-world financial
applications. BizFinBench consists of 6,781 well-annotated queries in Chinese,
spanning five dimensions: numerical calculation, reasoning, information
extraction, prediction recognition, and knowledge-based question answering,
grouped into nine fine-grained categories. The benchmark includes both
objective and subjective metrics. We also introduce IteraJudge, a novel LLM
evaluation method that reduces bias when LLMs serve as evaluators in objective
metrics. We benchmark 25 models, including both proprietary and open-source
systems. Extensive experiments show that no model dominates across all tasks.
Our evaluation reveals distinct capability patterns: (1) In Numerical
Calculation, Claude-3.5-Sonnet (63.18) and DeepSeek-R1 (64.04) lead, while
smaller models like Qwen2.5-VL-3B (15.92) lag significantly; (2) In Reasoning,
proprietary models dominate (ChatGPT-o3: 83.58, Gemini-2.0-Flash: 81.15), with
open-source models trailing by up to 19.49 points; (3) In Information
Extraction, the performance spread is the largest, with DeepSeek-R1 scoring
71.46, while Qwen3-1.7B scores 11.23; (4) In Prediction Recognition,
performance variance is minimal, with top models scoring between 39.16 and
50.00. We find that while current LLMs handle routine finance queries
competently, they struggle with complex scenarios requiring cross-concept
reasoning. BizFinBench offers a rigorous, business-aligned benchmark for future
research. The code and dataset are available at
https://github.com/HiThink-Research/BizFinBench.