The Role of Financial and Macroeconomic Factors in Credit Risk Prediction: A Machine Learning Approach for EU Countries
The sustainability of financial stability in the banking system is subject to the management of credit risk and the maintenance of the asset quality. The research study aims to analyze the ratios of non-performing loans in relation to the 27 member countries of the European Union (EU) during the period of 2014–2024 with the application of machine learning techniques. The originality of the study is embedded in the development of an integrated and comprehensive framework that does not confine credit risk to macroeconomic variables but also considers bank-specific variables, financial inclusion, and financial depth. During the analysis, the performance of the Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Extra Trees (ET), and Gradient Boosting (GB) algorithms was tested. From the analysis, it was evident that the SVR model offers the highest prediction accuracy. The results of feature significance analysis indicate that labor market variables, such as unemployment and employment, and return on equity (ROE), are critical variables for risk formation. Moreover, it has been identified that the use of financial inclusion and institutional structure variables enhances the model’s predictive capabilities. In conclusion, it has been established that this research supports the complex dynamics regarding the credit risk, while providing policymakers with a framework for a highly sensitive early warning system that meets International Financial Reporting Standard 9 (IFRS 9) standards.
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