ASSESSMENT OF CORPORATE FINANCIAL SECURITY USING MACHINE LEARNING METHODS
Abstract
This article investigates the development and comparative analysis of advanced machine learning methodologies for bankruptcy prediction, utilizing financial indicators from a comprehensive dataset of Ukrainian companies. Additionally, it provides practical recommendations for the application of these models in domestic enterprises, specifically within the context of crisis management. The study addresses various methods and models from both international and domestic literature for evaluating financial security and bankruptcy probability, including well-known models by E. Altman and R. Liss, as well as models by O. Tereshchenko and A. Matviychuk. The article proposes a machine learning solution using a dataset of 570 Ukrainian companies, balanced and encompassing 22 financial indicators for the period of 2014 to 2018. The study employs classic Logistic Regression and four advanced algorithms - Random Forest, XGBoost, SVM, and neural networks. The quality of models was evaluated using F-beta 2, ROC AUC, and Accuracy metrics. The article emphasizes the importance of Recall in evaluating the models, as missing a bankruptcy prediction can be more detrimental than a false positive. The results show that, with proper parameter adjustments and regularization to avoid overfitting, XGBoost performs the best, making it a highly effective tool for predicting company bankruptcies. The findings underscore XGBoost superior predictive accuracy and stability, which is crucial for reducing risks and enhancing the financial stability of enterprises. The model's high accuracy and relevance offer substantial potential for practical applications in the financial sector, enterprise management, investment activities, and public policy. Implementing these advanced machine learning methods in Ukraine's relatively unstable economic conditions could provide critical support for maintaining business stability and fostering economic growth. The core of the research highlights the importance of financial security for businesses as a cornerstone of their economic stability, emphasizing that it allows for the identification of potential threats and risks, enabling timely and effective solutions through the company's strengths to ensure sustainable financial health and future growth.
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