Enterprise Financial Risk Prediction: An In-Depth Analysis

Abstract

With the rapid development of the economy, enterprises are facing increasingly significant financial risks, making accurate financial risk prediction a crucial issue in corporate management. Therefore, the study of financial risk prediction models based on big data analysis technology is particularly necessary. This paper examines the critical role of financial risk prediction in contemporary enterprise management, particularly in the context of big data analysis technology. As companies face various financial risks, including liquidity, credit, and market risks, accurate prediction models become essential for informed decision-making and strategic planning. The study highlights the significance of both internal factors, such as operational management capability, and external factors, including macroeconomic conditions and industry characteristics, in influencing financial risk. It explores the application of machine learning and deep learning techniques, such as Decision Trees (DT) Long Short-Term Memory networks (LSTM), in developing robust financial risk prediction models. Despite the advancements in these technologies, the paper identifies existing limitations, including model complexity, interpretability issues, and adaptability to changing market conditions. Future research directions are proposed, emphasizing the need for enhanced model transparency, methodological diversification, and comprehensive macroeconomic analysis.