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| **FCMBench** is a multimodal benchmark for credit-risk–oriented workflows. It aims to provide a standard playground to promote collaborative development between academia and industry and provides standardized datasets, prompts, and evaluation scripts across multiple tracks (image, video, speech, agents, etc.) |
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| [简体中文](./README_cn.md) |
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| ## 🔥 News |
| - 【**2026. 03. 16**】✨ We released **FCMBench-V1.1**. This version adds English document images and corresponding QA pairs, expands the covered document types to 26, and increases the dataset to 5,198 images and 13,806 QA samples. |
| - 【**2026. 01. 01**】We are proud to launch **FCMBench-V1.0**, which covers 18 core certificate types, including 4,043 privacy-compliant images and 8,446 QA samples. It involves 3 types of Perception tasks and 4 types of Reasoning tasks, which are cross-referenced with 10 categories of robustness inferences. All the tasks and inferences are derived from real-world critical scenarios. |
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| > **Status:** Public release (v1.1). |
| > **Maintainers:** [奇富科技 / Qfin Holdings](https://github.com/QFIN-tech) |
| > **Contact:** [yangyehuisw@126.com] |
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| ## Tracks Overview |
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| ### 1) Vision-Language Track (✅ Available) |
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| Image-based financial document understanding: |
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| - **Entry:** [Vision-Language Track](vision_language) |
| - **Inputs:** document images + text prompts (JSONL, one sample per line) |
| - **Outputs:** text responses (JSONL, one sample per line) |
| - **Evaluation:** [Evaluation Script](vision_language/evaluation.py) |
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| #### Paper & Project Links |
| - [**Paper (arXiv)**](https://arxiv.org/abs/2601.00150) |
| - [**Paper (PDF)**](https://github.com/QFIN-tech/FCMBench/tree/main/TechnicalReport) |
| - [**Project Page**](https://github.com/QFIN-tech/FCMBench/tree/main/vision_language) |
| - [**Leaderboard**](https://qfin-tech.github.io/FCMBench) |
| - [**Sample Data**](https://qfin-tech.github.io/FCMBench/Examples.html) |
| - [**Dataset (ModelScope)**](https://modelscope.cn/datasets/QFIN/FCMBench-Data) |
| - [**Dataset (Hugging Face)**](https://huggingface.co/datasets/QFIN/FCMBench-Data) |
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| #### Reference Model Demo |
| We also provide access to an interactive demo of our Qfin-VL-Instruct model, which achieves strong performance on FCMBench. |
| If you are interested in trying the Gradio demo, please contact [yangyehui-jk@qifu.com] with the following information: |
| - Name |
| - Affiliation / Organization |
| - Intended use (e.g., research exploration, benchmarking reference) |
| - Contact email |
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| Access will be granted on a case-by-case basis. |
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| ### 2) Video Understanding Track (🕒 Coming Soon) |
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| ### 3) Speech Understanding & Generation Track (🕒 Coming Soon) |
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| ### 4) Multi-step / Agentic Track (🕒 Coming Soon) |
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| ## Citation |
| ``` |
| @misc{yang2026fcmbenchcomprehensivefinancialcredit, |
| title={FCMBench: A Comprehensive Financial Credit Multimodal Benchmark for Real-world Applications}, |
| author={Yehui Yang and Dalu Yang and Wenshuo Zhou and Fangxin Shang and Yifan Liu and Jie Ren and Haojun Fei and Qing Yang and Yanwu Xu and Tao Chen}, |
| year={2026}, |
| eprint={2601.00150}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2601.00150}, |
| } |
| ``` |
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| ## Star History |
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| [](https://www.star-history.com/#QFIN-tech/FCMBench&type=date&legend=top-left) |
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