The issue of determining the financial condition of commercial banks and separating investment-attractive banks from problem banks on this ground is extremely important for developing countries. The aim of this study is to make sure on the example of Ukraine that commercial banks really form separate clusters, where more reliable, stable and efficient banks are well separable from less successful ones in this regard. The study used the t-SNE and UMAP dimensionality reduction algorithms, and the Ward's Agglomerative Hierarchical Clustering algorithm. The results of visual analysis of two-dimensional t-SNE projections show that banks of different degrees of risk are well separable and have their own specifics. Clustering in the UMAP algorithm allowed distinguishing clusters with banks of Class A, "mid-tier" and problematic banks by different parameters. The t-SNE and UMAP algorithms for solving the problem are compared. The results show that a purely visual analysis of the two-dimensional map for the banks over the last period is best made using the t-SNE algorithm. UMAP, on the other hand, is proved to be excellent when used in tandem with the clustering algorithm.
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