The study is dedicated to the development of an early warning system for detecting crisis phenomena within banking institutions. Particular attention is given to the application of neural network technologies, specifically Kohonen self-organizing maps, for analyzing financial reporting data from Ukrainian banks. The research demonstrates that banks which have been liquidated tend to cluster into neighboring groups on the Kohonen map, indicating a high potential for forecasting the onset of financial instability before its direct manifestation. Through the use of advanced machine learning techniques, the proposed methodology allows for the timely identification of risks associated with a bank’s financial health, thus providing an opportunity to take preventive measures. This approach is particularly valuable for external stakeholders—such as investors, depositors, and regulatory bodies—who rely on publicly available financial information to assess the credibility and solvency of banking institutions. The developed tools not only enhance the precision of early crisis detection but also contribute to strengthening financial system stability by improving the overall risk monitoring processes. The findings emphasize the necessity of integrating innovative technologies into the risk management systems of banks and provide practical recommendations for their implementation in the Ukrainian banking sector.