Stress testing as an instrument of risk evaluation is actively used in many international organizations, as well as by central banks in many countries. Some organizations (including the Bank of Russia) when conducting stress testing do not publish results of the tests, though they are interesting for the business community. They are reticent so to avoid causing panic on markets which could lead to a massive outflow of deposits from the banking sector as a whole or from some individual banks in particular. As a rule, stress testing is conducted relying on huge number of unpublished reporting forms, but the business community has no access to them. Only four reporting forms are presented on the Bank of Russia’s website. In this paper we propose a simplified algorithm of credit risk stress testing of a banking cluster based on the four officially published reporting forms. The algorithm provides modelling of median values of banking variables depending on macroeconomic indicators, and subsequent retranslation of the received values for assessing the financial position of each bank included in the cluster. It is assumed that growth rates of banking indicators obtained from the econometrics models relying on median values are the same for each bank in the cluster. As of 1 January 2018, credit risk stress testing was conducted for 26 banks, nine of which are system-significant credit institutions. Within the stress testing, eight econometric time series models were developed. As a result, it was discovered that 11 out of 26 banks in the cluster will face certain difficulties regarding statutory requirements related to capital ratios or buffers.