FCMBench: A Comprehensive Financial Credit Multimodal Benchmark for Real-world Applications

📝 Original Info

• Title: FCMBench: A Comprehensive Financial Credit Multimodal Benchmark for Real-world Applications
• ArXiv ID: 2601.00150
• Date: 2026-01-01
• Authors: Yehui Yang, Dalu Yang, Wenshuo Zhou, Fangxin Shang, Yifan Liu, Jie Ren, Haojun Fei, Qing Yang, Yanwu Xu, Tao Chen

📝 Abstract

As multimodal AI becomes widely used for credit risk assessment and document review, a domainspecific benchmark is urgently needed that (1) reflects documents and workflows specific to financial credit applications, (2) includes credit-specific understanding and real-world robustness, and (3) preserves privacy compliance without sacrificing practical utility. Here, we introduce FCMBench-V1.0 -a large-scale financial credit multimodal benchmark for real-world applications, covering 18 core certificate types, with 4,043 privacy-compliant images and 8,446 QA samples. The FCMBench evaluation framework consists of three dimensions: Perception, Reasoning, and Robustness, including 3 foundational perception tasks, 4 credit-specific reasoning tasks that require decision-oriented understanding of visual evidence, and 10 real-world acquisition artifact types for robustness stress testing. To reconcile compliance with realism, we construct all samples via a closed synthesis-capture pipeline: we manually synthesize document templates with virtual content and capture scenarioaware images in-house. This design also mitigates pre-training data leakage by avoiding ...

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