Frontiers in FinTech: Multimodal Foundation Models for Financial Reporting and Decision Science

2026-08-23

AI Summary

研究通过FinVision系统，利用多模态大语言模型处理异构财务数据，提高估值准确性和决策效率。

Abstract

Heterogeneous financial data spanning PDF reports, Excel statements, chart images, and scanned policy documents challenge accounting information systems (AIS). This study introduces FinVision, a multimodal large language model (MLLM) system integrating vision-language models with domain-specific financial reasoning. Three innovations: (1) multimodal document intelligence with an automated cross-modal consistency validator mirroring audit evidence corroboration; (2) domain-adaptive two-stage training mastering valuation methodologies (DCF, P/E, P/B, P/S); and (3) a natural-language decision pipeline integrating modern portfolio theory, real-time risk monitoring, and multi-turn dialogue. Validation on 200 listed companies shows a 19 percent reduction in valuation error, and a user study with 48 professionals shows a 51 percent reduction in task completion time. Implications for audit automation, financial reporting quality, and democratized expert-level analysis are discussed.

Problem

• heterogeneous financial data
• accounting information systems
• multimodal document intelligence

Innovation

• multimodal document intelligence
• cross-modal consistency validator
• domain-adaptive two-stage training
• natural-language decision pipeline