I. Kurochkina, E. Shuvalova, L. Mamatova, I. Kalinin
Thursday 8 November 2018 by Libadmin2018


A new quality of economic growth is being formed and related to the development of digital computer technologies in the modern world. The government of the Russian Federation has taken a course to transfer the Russian economy to a digital basis. It is reflected in the program approved in 2017 "Digital Economy".
Among the main end-to-end digital technologies within this program are neurotechnologies, which, together with other advanced technologies, are created to reduce the gap with the leading countries in the near future and strengthen Russia’s competitive position in the future. Neural network technologies allow to solve a wide range of problems that have practical importance. Particularly, the neural networks have been successfully used in diagnosing and forecasting the financial condition of companies of various types and activities.
The housing and communal complex functioning of the Russian economy does not meet the requirements that ensure a high quality of people’s life. For this reason, the improvement of the complex activity is included among the most important social and economic priorities in the Russian Federation. The article presents the analytical indicators of the housing and communal activity services of Russia which is calculated on the basis of the Federal State Statistics Service official information by the authors. They testify to a number of positive trends and the existence of fundamentally important problems that need to be solved on the basis of modern adequate methods.
This article is devoted to the urgent problem of developing effective tools for diagnosing the financial condition of housing and communal sector companies. The authors proposed a universal neural model of financial performance, applicable to such companies. The model includes 15 factors with direct and indirect effect, such as revenues from the provision of housing and public utilities, the cost of these services, the average age of apartment buildings, the qualifications of employees, etc. A computer program has been developed to automatically calculate the proposed model. The model was tested in conditions of really functioning companies in the sphere of housing and public utilities. The advantages and disadvantages of this model are highlighted.
As a result of the implementation of the proposed developments, management companies engaged in housing and public utilities received an adequate tool for diagnosing and forecasting their financial condition. The visualization of economic processes has significantly increased. Management staff has received additional opportunities to make decisions to make reasonable decisions that are able to increase the company’s competitiveness.

Keywords: management of financial condition, financial result, housing and public utilities, methods of factor analysis, model of financial management, neural networks

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