The profitability and sustainability of the bank in the long term is largely determined by the quality of its management. Recognition of the importance of this factor is, for example, its inclusion as one of the components in the CAMELS rating system, which has not lost its popularity so far. At the same time, a direct quantitative assessment of the quality of management is poorly formalized and still represents a serious creative task. In this regard, an indirect assessment of this factor based on data envelopment analysis (DEA) has become popular. It allows you to build a production boundary for organizations implementing a technological process, the input of which receives several types of resources, and the product is also a multidimensional quantity. The distance from the point representing the organization to the production boundary determines the magnitude of its inefficiency and can be considered as a metric characterizing the quali­ty of management. The specification of the DEA model – the choice of input and output indicators – reflects the definition by the expert of the concept of the effectiveness of the organization and significantly affects the value of its assessment. Different specifications can lead to conflicting estimates of efficiency and, consequently, the quality of management.

In this study, for a fixed set of potential inputs and outputs, an assessment of the overall efficiency of banks is constructed, which accumulates the properties of estimates of partial efficiencies obtained for specific specifications of DEA models. An increase in the value of this estimate is associated with an increase in the values of estimates of partial efficiencies and vice versa. Thus, the resulting metric can act as an assessment of the quality of management of banks for a variety of possible definitions of their effectiveness. The overall efficiency metric allows banks to be ranked regardless of the specific specification of the DEA model, even at the production boundary, where all banks have the same maximum efficiency value. In addition, the study obtained additional metrics that allow analyzing the strategy for improving the efficiency (quality of management) for each bank. Each metric corresponds to a unique strategy. The number of variants of potential specifications of DEA models and the number of additional metrics introduced determines the depth of analysis. Using these metrics, you can get an answer to the question of exactly how a particular bank has reached the current level of overall efficiency. The authors also propose a method for comparing different DEA models based on the analysis of the relationship of the corresponding private efficiency with the metrics of overall efficiency and metrics of strategies to improve it. Quantitative results were obtained for a sample of open statements of medium-sized Russian banks that completed the reporting period without losses.