The starting point for the research has been the list of 147 banking crises within the period 1976–2011 prepared by the International Monetary Fund. The countries with crises have been analysed with respect to publicly available World Bank indicators in the periods of three years before the crises. The machine learning methodology for subgroup discovery has been used for the analysis. It enabled identification of five subsets of crises. Two of them have been identified as especially useful for the characterization of EU countries with banking crises in the year 2008. Fast growing credit activity is characteristic for the first subgroup while socioeconomic problems recognized by non-increasing quality of public health are decisive for the second subgroup. Comparative analysis of EU countries included into these subgroups demonstrated statistically significant differences with respect to World Bank good governance indicator values for the period before the crisis. Control of corruption, rule of law, and government effectiveness are the indicators which are statistically different for these sets of countries. The significance of the result is in the segmentation of the corpus of countries with banking crises and the recognition of connections between banking crises, socioeconomic problems, and governance effectiveness in some EU countries.The authors present the methodology and the results of their paper in the talk now available as a video:http://videolectures.net/ktsymposium2013_gamberger_modeling/