Research on the Application of Artificial Intelligence Technology in Enterprise Financial Risk Warning - Based on Machine Learning Method

Abstract

This research addresses the constraints inherent in conventional financial risk management by formulating and assessing a machine learning (ML)-driven framework for enterprise financial risk warning. Drawing upon a comprehensive dataset comprising 50,000 monthly financial records from over 200 enterprises across a five-year period, the study implemented and evaluated multiple ML algorithms, including Random Forest, XGBoost, and Neural Networks. The proposed methodology mitigates common challenges associated with financial data by employing advanced pre-processing techniques, robust feature engineering, and strategies for resolving class imbalance. Empirical analysis indicates notable enhancements compared to traditional methodologies. Specifically, Deep Neural Networks attained 96% precision and 92% recall in fraud detection; ML-based clustering methods achieved a 20% reduction in default rates; and Reinforcement Learning yielded a 12% increase in portfolio optimisation returns. Validation through real-world case studies further substantiates these outcomes. One implementation successfully forecasted liquidity shortages with 92% accuracy up to three months in advance, while another identified US$10 million in unauthorised transactions within six months. The findings underscore the potential of ML-based systems not only to improve predictive accuracy but also to deliver actionable risk mitigation strategies and to enhance operational efficiency by reducing false positives by 15% and enabling real-time alert processing. This research contributes to the advancement of enterprise risk management by offering a pragmatic implementation framework that addresses key challenges of transparency and scalability associated with AI integration in financial decision-making.