Development of a methodology for assessing the financial condition of credit institutions using machine learning algorithms and its economic justification

the article discusses the urgent problem of increasing the efficiency and proactivity of assessing the financial condition of credit institutions in the context of increasing volatility of financial markets and the complexity of banking operations. The fundamental limitations of traditional financial analysis techniques are analyzed, such as their lagging nature and inability to identify nonlinear dependencies, which justifies the urgent need to introduce modern machine learning algorithms. A comprehensive methodology is proposed that covers the stages from the systematic collection and intelligent preparation of data from public financial statements and macroeconomic sources to the construction of highly accurate predictive models using ensemble machine learning algorithms, in particular gradient boosting. The architecture of the predictive model, its evaluation metrics, and the principles of the feature system formation are described in detail. Experimental testing of the methodology was carried out on hypothetical but realistic data from Russian credit institutions, demonstrating excellent accuracy in predicting financial stability and default risks, as well as early detection of problems. A comprehensive economic justification for the implementation of the developed solution for banks, regulators and investors is presented, and its ability to reduce losses from insolvency, optimize management decisions and increase the overall financial stability of the banking system is emphasized.

financial condition, credit institutions, machine learning, gradient boosting, financial stability, risk assessment, banking supervision, economic impact, crisis prevention, digitalization of finance