This paper presents results from an econometric analysis of Russian bank defaults during the period 1997–2003, focusing on the extent to which publicly available information from quarterly bank balance sheets is useful in predicting future defaults. Binary choice models are estimated to construct the probability of default model. In the first part of the paper we analyse bank survival over the financial crisis of 1998. We find that preliminary expert clustering or automatic clustering improves the predictive power of the models and incorporation of macrovariables into the models is useful. Heuristic criteria are suggested to help compare model performance from the perspectives of investors or banks supervision authorities. In the second part of the paper we use the probability of default models developed in the first part in rolling windows to analyse the Russian banking system trends after the crisis 1998.