APPLICATION OF MACHINE LEARNING MODELS IN ASSESSING THE FINANCIAL STABILITY OF SMALL AND MEDIUM-SIZED ENTERPRISES

Financial stability of small and mediumsized enterprises (SMEs) is a key factor determining their adaptability, investment attractiveness, and capacity for sustainable development under conditions of economic volatility. The relatively low share of SMEs in the structure of the Russian economy is partly due to the limited effectiveness of traditional methods for assessing their financial condition. The purpose of this article is to substantiate the potential of applying machine learning models to assess the financial stability of SMEs. The methodological basis of the study includes an analysis of regulatory SME criteria, a review of financial stability factors, and an overview of current machine learning methods used in financial analytics. The article highlights the advantages of intelligent algorithms over classical methods: the ability to process incomplete and heterogeneous data, uncover hidden dependencies, deliver high prediction accuracy, and offer scalable solutions. Authorial recommendations are proposed for the use of machine learning models in the analysis of SME stability, including the development of specialized models, creation of digital platforms, implementation of hybrid methods, and educational support. The study concludes that the integration of machine learning into SME financial assessment practices is essential for increasing the transparency, efficiency, and resilience of this sector.