@inproceedings{liu-etal-2025-visfineval,
    title = "{V}is{F}in{E}val: A Scenario-Driven {C}hinese Multimodal Benchmark for Holistic Financial Understanding",
    author = "Liu, Zhaowei  and
      Guo, Xin  and
      Xia, Haotian  and
      Zeng, Lingfeng  and
      Lou, Fangqi  and
      Niu, Jinyi  and
      Li, Mengping  and
      Qi, Qi  and
      Li, Jiahuan  and
      Zhang, Wei  and
      Wang, Yinglong  and
      Cai, Weige  and
      Shen, Weining  and
      Zhang, Liwen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1229/",
    pages = "24099--24157",
    ISBN = "979-8-89176-332-6",
    abstract = "Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the first large-scale Chinese benchmark that spans the full front-middle-back office lifecycle of financial tasks. VisFinEval comprises 15,848 annotated question{--}answer pairs drawn from eight common financial image modalities (e.g., K-line charts, financial statements, official seals), organized into three hierarchical scenario depths: Financial Knowledge {\&} Data Analysis, Financial Analysis {\&} Decision Support, and Financial Risk Control {\&} Asset Optimization. We evaluate 21 state-of-the-art MLLMs in a zero-shot setting. The top model, Qwen-VL-max, achieves an overall accuracy of 76.3{\%}, outperforming non-expert humans but trailing financial experts by over 14 percentage points. Our error analysis uncovers six recurring failure modes{---}including cross-modal misalignment, hallucinations, and lapses in business-process reasoning{---}that highlight critical avenues for future research. VisFinEval aims to accelerate the development of robust, domain-tailored MLLMs capable of seamlessly integrating textual and visual financial information. The data and the code are available at https://github.com/SUFE-AIFLM-Lab/VisFinEval."
}