@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding
%A Liu, Zhaowei
%A Guo, Xin
%A Xia, Haotian
%A Zeng, Lingfeng
%A Lou, Fangqi
%A Niu, Jinyi
%A Li, Mengping
%A Qi, Qi
%A Li, Jiahuan
%A Zhang, Wei
%A Wang, Yinglong
%A Cai, Weige
%A Shen, Weining
%A Zhang, Liwen
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F liu-etal-2025-visfineval
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1229/
%P 24099-24157
Markdown (Informal)
[VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding](https://aclanthology.org/2025.emnlp-main.1229/) (Liu et al., EMNLP 2025)
ACL
- Zhaowei Liu, Xin Guo, Haotian Xia, Lingfeng Zeng, Fangqi Lou, Jinyi Niu, Mengping Li, Qi Qi, Jiahuan Li, Wei Zhang, Yinglong Wang, Weige Cai, Weining Shen, and Liwen Zhang. 2025. VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 24099–24157, Suzhou, China. Association for Computational Linguistics.