Economic convergence and digitalization are the most important trends of the modern community development. It is their interaction that creates new opportunities for the improvement of competitive capability and performance in the framework of joint business conducted by representatives of the wide variety of segments and sectors of economy. In response to digitalization, an ecosystem becomes the main institutional and organizational form of business in the framework of inter-sectoral economic convergence. The purpose of this article is to define more exactly the concept of an ecosystem as a form of doing joint business in the environment of inter-sectoral economic convergence and digitalization, and to build a classification of ecosystems. The following hypothesis is put forward in the study: an ecosystem as an institutional and organizational form of doing joint business is the result of the concurrent impact of inter-sectoral economic convergence and digitalization thereon, and the "connection of the base product provided to customer by the inter-sectoral economic convergence initiator prior to creating an ecosystem with digital and/or information technology" may be a criterion for classification of economic ecosystems. The novelty of the approach is as follows. The consideration of an ecosystem as a form of doing joint business simultaneously influenced by economic inter- sectoral convergence and digitalization has made it possible to define more exactly the concept of an ecosystem, identify an ecosystem parameter to be applied for classification of ecosystems and the main characteristic thereof the values of which may be used to classify economic ecosystems.

The article contains the study of the experience of operation of the specific Sber financial ecosystem as a new form of entrepreneurial activity in the competitive economic environment which is driven by the impact of digitalization on the economic convergence processes – the modern trend in the social development in general. The study of the experience of the Sber financial ecosystem which is one of the most highly developed ones in Russia is both of theoretical and practical interest. The purpose of the article is to describe the actual experience of Sber ecosystem’s operation. The results of the performed analysis are as follows. Definitely the Sber ecosystem is a form of organization of joint business implemented in the framework of intersectoral convergence driven by digitalization. The impact of intersectoral convergence is manifested in the fact that the creation of the ecosystem was initiated by a financial institution - the largest Russian savings bank; and the participants in this ecosystem are representatives of a wide variety of sectors and segments of the economy. The impact of digitalization shows in the fact that the basis of joint business is a modern digital base which includes IT, IT platforms and networks. The modern mathematical and instrumental methods of data processing and IT startups are not only the digital specifics of the ecosystem functioning, but also effective tools to attract to a joint business – on a voluntary basis only – the partners from various fields of activity, and provide the Sber ecosystem with undoubted competitive advantages.

For a comprehensive assessment of the management decisions quality, it is necessary to take into account heterogeneous information presented both in numerical form and in natural language expressions. The effective occurs the use of data mining including neural network clustering and fuzzy set theory. The article presents our approach to the use of these methods for evaluating risks and the management decisions quality in Russian higher education on the example of the implementation of the most ambitious Project 5-100 for it. On the example, the expediency of the neural network clustering to assess the possibility of achieving the goals of any such large-scale project has been proved. Clustering the information database used for the analysis, makes it possible to carry out an objective selection of candidate universities-candidates for the right to receive state subsidies, as well as to adjust the composition of the Project participants. Another methods of intellectual analysis – the construction of a complex of fuzzy inference systems, – confirmed the possibility of a quantitative fi evaluating of the project based on the expert verbal estimates of the project. At the same time, the neural network clustering initially illustrated the unattainability of the Project 5-100 goals. The use of a complex of fuzzy inference systems confirmed this statement by the very low quantitative final assessment of the project on the basis of verbal expert opinions.

Solving the problems of effective business management is associated with a variety of current goals facing the same and, by implication, requires the construction of appropriate models of efficient business. The article presents two problems of doing business which, apart from their common target being an improvement of business efficiency, have different current goals. The creation or development of any business involves the construction of a specific business plan for it, including a list of those areas of business development, the implementation of which will increase its efficiency. The first problem considered in the article is related to the phased implementation of all areas of efficiency improvement in order to ultimately obtain the greatest efficiency of their realization. The second one solves the problem of increasing efficiency by partially implementing efficiency improvement directions from the initial list, taking into account certain limitations, for example, in conditions of limited company resources. For the construction of models which would meet the problems set, an efficiency criterion is substantiated and proposed in the article, and Algorithms 1 and 2 are developed which made it possible to build the efficient business models which take into account the difference in its current goals. The authors have developed a multi-stage Algorithm 1 for the generation of individual sets of areas for improvement of efficiency to be used to solve the tasks at hand. Algorithm 2 implemented at each stage of Algorithm 1 has been developed by the authors by using the Pareto optimality method but supplemented by taking into account the features and objectives of the current tasks set for the business. The use of such algorithms has made it possible to build efficient business models enabling not only to obtain an economic effect inherent to each efficiency improvement area, but also to ensure additional growth thereof driven by the properties of the developed algorithms.