Research on Enterprise Financial Risk Early Warning Based on BP Neural Network

Abstract

With the ongoing advancement of big data technology and the increasing uncertainty in the economic environment, enterprises are encountering heightened financial risks. Consequently, timely and accurate financial risk warnings have become a critical focus for both managers and investors. This article examines the financial risk warning mechanisms for real estate listed companies, utilizing the BP neural network model to assess and analyze these risks. The research involves several key steps: First, financial data from real estate listed companies is collected and organized. Second, drawing on financial management theories, factor analysis identifies six critical indicators—development capability, debt repayment ability, profitability, cash flow capability, operational efficiency, and stability—as determinants of corporate financial risk. Subsequently, the BP neural network model is employed to iterate and analyze the financial risks of these companies, ultimately providing early warning signals. The model's prediction accuracy reaches 85.31%, demonstrating its effectiveness in early financial risk detection. In summary, within the realm of modern big data technology, the BP neural network model offers significant advantages for financial risk warning in enterprises, providing valuable theoretical insights and practical guidance for similar organizations.