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.)

🤗 Hugging Face   |   🤖 ModelScope   |   📑 FCMBench Paper   |   📑 FCMBench-Video Paper   |   🏆 Leaderboard   |   🌐 简体中文

🔥 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.
• 【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

Entry	Inputs	Outputs	Evaluation Script	Leaderboard	Paper	Sample Data
Vision-Language Track	document images + text prompts (JSONL, one sample per line)	text responses (JSONL, one sample per line)	evaluation.py	Leaderboard	arXiv 2601.00150	Examples
Video Understanding Track	document videos + text prompts (JSONL)	text responses (JSONL)	benchmark_eval.py	via submission	arXiv 2604.25186	see README

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1) Vision-Language Track (✅ Available)

Image-based financial document understanding.

Sample Data

Preview sample images and QA examples on the Examples page.

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.

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2) Video Understanding Track (🎬 Available)

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 for full benchmark details and evaluation results on nine Video-MLLMs.

Sample Data

Please refer to the Video Understanding track README 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.

Reference Model Demo

(TBD)

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

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

Citation

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},
}

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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