Justification of the choice of indicators in models for assessing the level of economic security of innovative and active enterprises in conditions of uncertainty of the internal and external environment
Abstract
The article is devoted to substantiating the choice of financial indicators for discriminant and neural network models for diagnosing the economic security of innovative and active enterprises in the field of telecommunications in conditions of uncertainty of the internal and external environment. The importance of assessing the economic security of the system at any level is unquestionable, such assessments are the basis for decision-making not only on ensuring economic security, but also opportunities for system development, determination of necessary resources, creation and use of system reserves, evaluating the effectiveness of the economic security system and the activities of the relevant division of the enterprise. It is established that due to a completely different tool base, the available approaches to assessing the economic security of the enterprise do not compete with each other. We can only talk about the different degree of development of approaches and their dissemination. It shows the importance of taking into account the phenomenon of “uncertainty” in the economic activity of innovative-active enterprises, which is closely related to the concept of “economic risk”, since any economic or economic activity of an enterprise is characterized by incompleteness of information about the presence of patterns, unpredictability of many economic phenomena and processes, the influence of a large number of interrelated and difficult to identify factors. The latest methodological foundations for assessing the economic security of innovative and active enterprises, which are based on the tools of Fuzzy Logic and neural networks, are proposed and shown. The fuzzy set method makes it possible to use the data selected for assessing the economic security of enterprises in their dynamics, which makes it possible to take them into account when making strategic management decisions. In addition, the use of modern analytical platforms allows for deep data preprocessing in order to check them for multicoleniarity, exclude random data, which takes into account the uncertainty of input and output variables as much as possible.
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