Financial Information Structuring with Multimodal Large Language Models: From Entity Tagging to Risk Explanation

Abstract

The exponential growth of unstructured data in the global financial sector has created an urgent need for advanced analytical systems capable of extracting, structuring, and interpreting complex information. Historically, financial natural language processing has focused predominantly on textual data, often neglecting the rich, multimodal information embedded in corporate reports, such as tabular data, charts, and graphical diagrams. This paper investigates the application of Multimodal Large Language Models to the domain of financial information structuring, presenting a comprehensive pipeline that spans from granular entity tagging to high-level risk explanation. By integrating visual and textual modalities, these models demonstrate an enhanced capacity to contextualize financial entities within their broader economic narratives. The methodology proposed outlines a systematic approach for multimodal data ingestion, cross-modal alignment, and relation extraction, culminating in a robust risk explanation framework. Through extensive analysis, we illustrate how the integration of vision and text encoders allows for the accurate disambiguation of financial concepts that are otherwise opaque to unimodal systems. The findings suggest that Multimodal Large Language Models significantly reduce the semantic gap between raw financial data and actionable intelligence, providing a scalable solution for automated risk assessment, compliance monitoring, and investment analysis