This article continues the series on the Altman model application to the largest Russian companies’ performance indicators, with the focus on the Altman model for the Russian industry for 2015. Parameters of the model are calculated in two ways: relying on the canonical model, and on the model for developing countries. The model for developing countries is seen as a preferable one, showing similar socio-economic characteristics for Russia. The author’s estimations are based on the official statistical data provided by the Federal State Statistics Service of the Russian Federation – Rosstat, with the statistical bias taken into account. Related statistical errors include seriously underestimated value of fixed assets, the erroneous industrial classification of Gazprom and Rosneft groups. Creation of offshore company accounts as collateral for creditors is also taken into account. A method for determining the size of these accounts is proposed. The resulting scores according to the Altman model for industry are compared with the calculation results of the Altman model applied to Rosneft and Gazprom Group performance indicators. The Altman model scores are also estimated for three industrial sectors: mining, manufacturing and power generation. Canonical Altman model, based on the data by Rosstat and considering statistical errors, forecasts unsatisfactory financial condition of the Russian industry. At the same time, these results are much better than in Rosneft and Gazprom group. According to the model for developing countries, the results are satisfactory. Consideration of offshore accounts allows the author to predict good financial condition of the industry of the Russian Federation. Research findings suggest that particularly good results are demonstrated by the mining and manufacturing industries; the findings on the power industry are considered to be least satisfactory ones – due to the restriction of electricity and heat tariffs. Conclusions are drawn concerning improvements of the economic analysis of industry, improving statistic methods and accounting in the industry of the Russian Federation.