Construction and Application of Financial Risk Early Warning System for Logistics Enterprises based on Big Data

Abstract

With the rapid development of the logistics industry, financial risks have become increasingly complex and dynamic, necessitating an efficient and intelligent early warning system. This study constructs a financial risk early warning system for logistics enterprises based on big data technology. By integrating multi-source financial data, operational metrics, and external economic indicators, the system employs machine learning algorithms and data mining techniques to assess and predict potential risks. A risk evaluation model is developed using key financial indicators such as liquidity, solvency, profitability, and operational efficiency. The system utilizes real-time data processing and predictive analytics to provide timely warnings, enabling enterprises to take proactive measures to mitigate financial crises. Empirical analysis demonstrates that the proposed system improves risk identification accuracy and enhances financial decision-making. The findings suggest that big data-driven financial risk management can significantly enhance the stability and resilience of logistics enterprises in a highly competitive market.