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--- |
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configs: |
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- config_name: benchmark |
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data_files: |
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- split: test |
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path: vision_language/FCMBench_v1.0_testset_full.jsonl |
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license: apache-2.0 |
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task_categories: |
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- table-question-answering |
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- text-classification |
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pretty_name: FCMBench |
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size_categories: |
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- 10K<n<100K |
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--- |
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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 |
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- 【**2026. 01. 01**】✨ We are proud to launch [**FCMBench-V1.0**](https://github.com/QFIN-tech/FCMBench/tree/main/vision_language), 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 (v1.0). |
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> **Maintainers:** [奇富科技 / Qfin Holdings](https://github.com/QFIN-tech) |
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> **Contact:** [yangyehui-jk@qifu.com] |
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--- |
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## Tracks Overview |
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### 1) Vision-Language Track (✅ Available, **FCMBench-V1.0**) |
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Image-based financial document understanding: |
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- **Entry:** [Vision-Language Track](https://github.com/QFIN-tech/FCMBench/tree/main/vision_language) |
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- **Inputs:** document images + text prompts (JSONL, one sample per line) |
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- **Outputs:** text responses (JSONL, one sample per line) |
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- **Evaluation:** [Evaluation Script](https://github.com/QFIN-tech/FCMBench/blob/main/vision_language/evaluation.py) |
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#### Paper & Project Links |
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- [**Paper (arXiv)**](https://arxiv.org/abs/2601.00150) |
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- [**Paper (PDF)**](https://arxiv.org/pdf/2601.00150) |
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- [**Project Page**](https://github.com/QFIN-tech/FCMBench/tree/main/vision_language) |
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- [**Leaderboard**](vision_language/LEADERBOARD.md) |
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- [**Dataset (ModelScope)**](https://modelscope.cn/datasets/QFIN/FCMBench-V1.0) |
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- [**Dataset (Hugging Face)**](https://huggingface.co/datasets/QFIN/FCMBench-V1.0) |
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#### Reference Model Demo |
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We also provide access to an interactive demo of our Qfin-VL-Instruct model, which achieves strong performance on FCMBench-V1.0. |
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If you are interested in trying the Gradio demo, please contact [yangyehui-jk@qifu.com] with the following information: |
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- Name |
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- Affiliation / Organization |
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- Intended use (e.g., research exploration, benchmarking reference) |
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- 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 |
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``` |
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@misc{yang2026fcmbenchcomprehensivefinancialcredit, |
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title={FCMBench: A Comprehensive Financial Credit Multimodal Benchmark for Real-world Applications}, |
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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}, |
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year={2026}, |
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eprint={2601.00150}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2601.00150}, |
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} |
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``` |
