IMPROVING CORPORATE FINANCIAL RISK PREDICTION THROUGH ARTIFICIAL INTELLIGENCE
Abstract
Increasing financial complexity requires more accurate and timely risk prediction. Traditional methods may struggle to capture nonlinear relationships and early signs of financial deterioration. This study examines how artificial intelligence can improve enterprise financial risk prediction through financial indicators, data preprocessing, feature selection, and machine learning algorithms. The proposed approach has the potential to improve prediction accuracy and support effective risk management and decision-making.References
1. President of the Republic of Uzbekistan. (2025, October 21). The implementation of artificial intelligence technologies to be elevated to a new level. Official website of the President of the Republic of Uzbekistan.
2. Altman E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.
3. Ohlson J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.
4. Zhao J., Ouenniche J., De Smedt J. (2024). Survey, classification and critical analysis of the literature on corporate bankruptcy and financial distress prediction. Machine Learning with Applications, 15, 100527.
5. El Madou K., Marso S., El Kharrim M., El Merouani M. (2024). Evolutions in ML technology for financial distress prediction: A comprehensive review and comparative analysis. Expert Systems, 41(2), e13485.
6. Lokanan M. E., Ramzan S. (2024). Predicting financial distress in TSX-listed firms using machine learning algorithms. Frontiers in AI, 7.
7. Deng S., Luo Q., Zhu Y., Ning H., Shimada T. (2024). Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies. Pacific-Basin Finance Journal, 85, 102393.
