QFIN/FCMBench-Data

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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🔥 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.

Status: Public release (v1.1).
Maintainers: 奇富科技 / Qfin Holdings
Contact: [yangyehuisw@126.com]

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Tracks Overview

1) Vision-Language Track (✅ Available)

Image-based financial document understanding:

• Entry: Vision-Language Track
• Inputs: document images + text prompts (JSONL, one sample per line)
• Outputs: text responses (JSONL, one sample per line)
• Evaluation: Evaluation Script

Paper & Project Links

• Paper (arXiv)
• Paper (PDF)
• Project Page
• Leaderboard
• Sample Data
• Dataset (ModelScope)
• Dataset (Hugging Face)

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

Access will be granted on a case-by-case basis.

2) Video Understanding Track (🕒 Coming Soon)

3) Speech Understanding & Generation Track (🕒 Coming Soon)

4) Multi-step / Agentic Track (🕒 Coming Soon)

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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