Construction and Comparative Analysis of Financial Risk Early Warning Model Based on Machine Learning

Amidst the progressing intensification of globalization and economic integration, the management of financial market volatility and risk has become a critical concern for businesses. The escalating complexity of financial crises has heightened the urgency for advanced early warning systems capable of accurately forecasting future risks. In this context, this study has developed a suite of financial risk early warning models leveraging machine learning techniques, encompassing Support Vector Machine (SVM), Random Forest (RF), and Deep Learning (DL) models, and conducted a thorough comparative analysis. The research utilized financial statement data and market transaction records from A-share listed companies spanning 2008 to 2022. Post the removal of multicollinearity, standardization, and outlier exclusion, a dataset was curated that included over 30 financial indicators such as the current ratio, debt-to-asset ratio, and net profit growth rate. A logistic regression model was applied for baseline comparison, revealing that machine learning models notably outperformed it across key metrics including accuracy, precision, and recall rates.The DL model, in particular, showcased enhanced predictive capabilities for financial risks, attributable to its proficiency in capturing non-linear features and its automated high-level feature extraction capabilities. Conversely, the RF model provided practical benefits in terms of feature interpretability, swift training, and the provision of feature importance scores. To bolster the models' adaptability and predictive accuracy in complex scenarios, the study proposes enhancements, advocating for an early warning mechanism within DL models that integrates multi-source heterogeneous data and dynamic financial indicators.Moreover, to address the dynamic nature of financial markets, this study has integrated a real-time assessment mechanism. This mechanism facilitates ongoing monitoring and prompt adjustment of model parameters in response to market fluctuations, ensuring the model's sustained efficacy and dependability. The generalizability of the models was substantiated through time-series cross-validation and replication across various industry subsamples, demonstrating their stability and robust performance. This research presents scientific financial risk assessment tools for corporate management and investors, laying the groundwork for future advancements in the domain.