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Company managers, financial institutions and insurers are constantly concerned about the solvency of their corporate clients. The increased demand for solvency analysis and higher computing capacity are driving the adoption of non-traditional methods. The present study examines firm insolvency from a geographically concentrated perspective, focusing on small and medium-sized enterprises in the Pécs and Budapest city regions. It builds on artificial intelligence and machine learning methods and provides a comparative analysis of several aspects of neural network, SVM, bagging, and random forest meta-methods. The authors finally attempt to open the 'black-box' phenomenon by assessing the importance of explanatory variables and identifying the most significant financial explanatory variables. [ABSTRACT FROM AUTHOR]