Dynamic Monitoring System Design for Enterprise Financial Liquidity Risk Based on Multimodal Data Fusion

Abstract

The traditional monitoring system for enterprise finance relies on a single structured financial data, which has a lag problem. This article proposes a design scheme for a dynamic monitoring system of enterprise financial liquidity risk that integrates multimodal data, and conducts theoretical analysis from the system architecture, multimodal data fusion model, and dynamic risk monitoring model. By comparing the theoretical adaptability of different data fusion strategies and risk assessment models, the advantages of combining hybrid fusion strategies with temporal neural networks are clarified. This system can integrate structured and unstructured text data as well as real-time transaction data, achieve dynamic perception and early warning of risks, and provide theoretical support for enterprise liquidity risk management.