Construction and Optimization of University Financial Risk Early Warning Model Based on Big Data Analysis

With the continuous expansion of the scale of universities, the importance of financial risk warning in universities is becoming increasingly prominent. The traditional financial risk warning work in universities still faces problems such as an incomplete warning indicator system and outdated risk warning information technology. This article is based on big data analysis and aims to construct and optimize a financial risk warning model for universities. Through in-depth research on the application of big data technology in the financial field, combined with the characteristics and laws of financial data in universities, an effective early warning model has been proposed. This model promptly identifies potential financial risks, helps university managers take targeted measures, effectively reduces the occurrence of risks, and ensures the stable development of university finance. By continuously optimizing the model, its sensitivity (Max: 0.927), specificity (Max: 0.899), and Matthews correlation coefficient (Max: 0.99) have been improved, providing important decision support for financial risk management in universities.