FinanceQA is a testing suite that evaluates LLMs' performance on complex numerical financial analysis tasks that mirror real-world investment work. Despite recent advances, current LLMs fail to meet the strict accuracy requirements of financial institutions, with models failing approximately 60% of realistic tasks that mimic on-the-job analyses at hedge funds, private equity firms, investment banks, and other financial institutions. The primary challenges include hand-spreading metrics, adhering to standard accounting and corporate valuation conventions, and performing analysis under incomplete information - particularly in multi-step tasks requiring assumption generation. This performance gap highlights the disconnect between existing LLM capabilities and the demands of professional financial analysis that are inadequately tested by current testing architectures. Results show that higher-quality training data is needed to support such tasks, which we experiment with using OpenAI's fine-tuning API. FinanceQA is publicly released at [this https URL](https://huggingface.co/datasets/AfterQuery/FinanceQA).
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models
IDEA Research（IDEA研究机构）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；The Hong Kong University of Science and Technology（香港科学与技术大学）；Nanjing University（南京大学）；South China Normal University（华南师范大学）；DataArcTech Ltd.（DataArcTech有限公司）
FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis
National University of Singapore（国立新加坡大学）；Estates Pte Ltd（6Estates公司）；Asian Institute of Digital Finance（亚洲数字金融研究所）；Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）
FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs
Columbia University（哥伦比亚大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Rensselaer Polytechnic Institute（拉特格斯理工学院）；University of Montreal（蒙特利尔大学）；McGill University（麦吉尔大学）；MBZUAI（麦吉尔大学人工智能研究所）；Cardiff University（卡迪夫大学）；The University of Manchester（曼彻斯特大学）；Harvard University（哈佛大学）
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Shanghai Institute of AI for Education, East China Normal University（华东师范大学上海人工智能教育研究院）；PICC AMC（中国人民保险资产管理公司）；Bytedance（字节跳动）
FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information
Georgia Institute of Technology（佐治亚理工学院）；Columbia University（哥伦比亚大学）；California State University（加州州立大学）；University of Montreal（蒙特利尔大学）；Carnegie Mellon University（卡内基梅隆大学）；Rensselaer Polytechnic Institute（莱斯利理工学院）；The University of Manchester（曼彻斯特大学）；Harvard University（哈佛大学）
FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs
The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；KTH Royal Institute of Technology（皇家理工学院）；Peng Cheng Laboratory（鹏城实验室）；Singapore Management University（新加坡管理大学）
How do standard time series forecasting benchmarks translate to finance? A broad study of risk adjusted performance
Machine Learning Research Group, Department of Engineering Science, University of Oxford（牛津大学工程科学系机器学习研究组）；Oxford-Man Institute of Quantitative Finance, University of Oxford（牛津大学量化金融研究所）
Evaluating LLMs in Finance Requires Explicit Bias Consideration
University of Chicago Booth School of Business（芝加哥大学商学院）；Pusan National University（釜山国立大学）；Ulsan National Institute of Science（乌山国立科学研究院）；University of Oxford（牛津大学）；University of Florida（佛罗里达大学）
Evaluating utility in synthetic banking microdata applications
Business School, University of Edinburgh（爱丁堡大学商学院）；Central Bank of Paraguay（巴拉圭中央银行）；Centre for Statistics, University of Edinburgh（爱丁堡大学统计中心）；Centre for Financial Innovations, University of Edinburgh（爱丁堡大学金融创新中心）
