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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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| <p align="center"> |
| 🤗 <a href="https://huggingface.co/datasets/QFIN/FCMBench-Data"><b>Hugging Face</b></a> | 🤖 <a href="https://modelscope.cn/datasets/QFIN/FCMBench-Data"><b>ModelScope</b></a> | 📑 <a href="https://arxiv.org/abs/2601.00150"><b>FCMBench Paper</b></a> | 📑 <a href="https://arxiv.org/abs/2604.25186"><b>FCMBench-Video Paper</b></a> | 🏆 <a href="https://qfin-tech.github.io/FCMBench"><b>Leaderboard</b></a> | 🌐 <a href="./README_cn.md"><b>简体中文</b></a> |
| </p> |
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| ## 🔥 News |
| - 【**2026. 04. 29**】🎬 We released **FCMBench-Video**, a benchmark for document-video intelligence. Built from 495 captured atomic videos and composed into 1,200 long-form videos with 11,322 QA instances across 28 document types (bilingual CN/EN). Paper: [arXiv 2604.25186](https://arxiv.org/abs/2604.25186). |
| - 【**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).<br> |
| > **Maintainers:** [奇富科技 / Qfin Holdings](https://github.com/QFIN-tech)<br> |
| > **Contact:** [yangyehuisw@126.com] |
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| --- |
|
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| ## Tracks Overview |
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| | Entry | Inputs | Outputs | Evaluation Script | Leaderboard | Paper | Sample Data | |
| |---|---|---|---|---|---|---| |
| | [Vision-Language Track](vision_language) | document images + text prompts (JSONL, one sample per line) | text responses (JSONL, one sample per line) | [evaluation.py](vision_language/evaluation.py) | [Leaderboard](https://qfin-tech.github.io/FCMBench) | [arXiv 2601.00150](https://arxiv.org/abs/2601.00150) | [Examples](https://qfin-tech.github.io/FCMBench/Examples.html) | |
| | [Video Understanding Track](video_understanding) | document videos + text prompts (JSONL) | text responses (JSONL) | [benchmark_eval.py](video_understanding/benchmark_eval.py) | via [submission](video_understanding/README.md#leaderboard) | [arXiv 2604.25186](https://arxiv.org/abs/2604.25186) | see [README](video_understanding/README.md) | |
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| --- |
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| ### 1) Vision-Language Track (✅ Available) |
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| Image-based financial document understanding. |
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| #### Sample Data |
| Preview sample images and QA examples on the [Examples page](https://qfin-tech.github.io/FCMBench/Examples.html). |
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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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| --- |
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| ### 2) Video Understanding Track (🎬 Available) |
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| Document-video intelligence benchmark covering document perception, temporal grounding, and evidence-grounded reasoning under realistic handheld capture conditions. Built from 495 captured atomic videos composed into 1,200 long-form videos (20s/40s/60s duration tiers) with 11,322 expert-annotated QA instances across 28 document types in bilingual Chinese/English settings. See the [paper](https://arxiv.org/abs/2604.25186) for full benchmark details and evaluation results on nine Video-MLLMs. |
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| #### Sample Data |
| Please refer to the [Video Understanding track README](video_understanding/README.md) for the full data composition, instruction file descriptions, and quickstart guide. A stratified 10% subset with ground-truth (`FCMBench-Video_v1.0_small.jsonl`) is available for self-evaluation. |
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| #### Reference Model Demo |
| *(TBD)* |
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| --- |
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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 |
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| **FCMBench (Vision-Language Track):** |
| ``` |
| @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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| **FCMBench-Video (Video Understanding Track):** |
| ``` |
| @misc{cui2026fcmbenchvideobenchmarkingdocumentvideo, |
| title={FCMBench-Video: Benchmarking Document Video Intelligence}, |
| author={Runze Cui and Fangxin Shang and Yehui Yang and Qing Yang and Tao Chen}, |
| year={2026}, |
| eprint={2604.25186}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2604.25186}, |
| } |
| ``` |
|
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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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