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]
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 |
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.
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)
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},
}