金融科技前沿：面向财务报告与决策科学的多模态基础模型
Frontiers in FinTech: Multimodal Foundation Models for Financial Reporting and Decision Science
AI总结：
针对异构财务数据对会计信息系统的挑战，本研究提出多模态大语言模型FinVision，经200家上市公司验证可降低19%估值误差，48名专业用户测试缩短51%任务时间，助力审计自动化与财务分析民主化。
AI中文摘要：
涵盖PDF报告、Excel报表、图表图像及扫描政策文档的异构财务数据，对会计信息系统（AIS）构成挑战。本研究推出FinVision，这一将视觉语言模型与特定领域财务推理相融合的多模态大语言模型（MLLM）系统，包含三项创新：（1）具备自动跨模态一致性验证器的多模态文档智能，该验证器可镜像审计证据印证流程；（2）掌握估值方法（DCF、P/E、P/B、P/S）的领域自适应两阶段训练；（3）整合现代投资组合理论、实时风险监测及多轮对话的自然语言决策流程。对200家上市公司的验证显示，估值误差降低19%；对48名专业人士的用户研究显示，任务完成时间缩短51%。本文还探讨了其在审计自动化、财务报告质量及民主化专家级分析方面的应用意义。
英文摘要：
Financial information no longer arrives in a single format. Research reports come as PDFs, financial statements live in spreadsheets, market trends are captured in images, and policy documents reach analysts as scans, each carrying part of the picture the others cannot supply. Accounting information systems built around single-modality extraction pipelines and rule-based tools therefore struggle to assemble the full picture, slowing financial statement analysis, complicating audit evidence corroboration, and limiting investment decision support. This study presents FinVision, a multimodal large language model that unites vision-language models with domain-specific financial reasoning. Instead of processing documents in isolation, FinVision reads text, tables, and images together, converts them into consistent structured data, and verifies cross-modal agreement, in the same spirit as auditors corroborating evidence from independent sources. The model is trained in two stages, pre-trained on large-scale public financial corpora and fine-tuned on institution-specific investment data, so it can apply established valuation methodologies and audit risk assessment frameworks while outperforming zero-shot and single-stage baselines. A natural-language decision pipeline lets users describe what they need and turns those descriptions into executable workflows, supporting portfolio optimization, real-time risk monitoring, and refinement through multi-turn dialogue. Across 200 listed companies, FinVision reduced valuation error by 19 percent relative to the strongest baseline; a user study with 48 accounting and investment professionals reported a 51 percent reduction in task completion time. These results carry implications for audit automation, financial reporting quality, and more inclusive access to expert-level financial analysis.
