Early detection of the financial condition of commercial banks is especially relevant in modern conditions of economic turbulence related to the global COVID-19 pandemic. The article provides a brief overview of the applied methods in the early detection of financial problems and prevention of bank insolvency. This study also explains the use and purpose of systems such as CAMELS and multivariate analysis models.The article illustrates the necessity of the complex use of multivariate analysis models and averaging simulation results in obtaining the most reliable results.A mechanism is offered for organizing the process of early prevention of bank insolvency.When implementing this mechanism, seven of the most well-known multivariate models, selected according to the criteria of successful application in world practice and the compatibility of their results, were used. For better compatibility of the results, some of them have been modified using the logistic function.Several mechanisms and methods were used to analyze the condition of a number of Russian banks to identify those banks which are highly likely to go bankrupt in the coming years.According to the suggested methods, use of these developments demonstrated that the analysis depicts the truthful overview of the bank’s condition and can be one of the good bank insolvency early prevention tools.
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