USING NEURAL NETWORKS IN MODELING THE FINANCIAL RESULTS OF BUSINESS PROCESSES

Nadiia YASYNSKA, Olena IVCHENKOVA

Анотація


Introduction. The attributes of neural networks are embodied in a study of the effectiveness of business processes, which is based on integrated coefficients of international monitoring with a range of quantitative parameters. Simulated situational precedents will allow to assume multivariate solutions in real time.

The purpose of the work is to use of neural network technologies in modeling financial results of business processes with integrated international monitoring indices and domestic statistics.

Results. The obtained sections of the response surface of the resulting indicator and pairs of independent variables for a neural network of type RBF 3–7–1 are characterized. An algorithm is proposed for applying the methodology for assessing the functioning of a business using neural network technologies.

Conclusions.

1. According to the results of theoretical generalizations, the understanding of the main purpose of the business operation has been improved. A feature of the proposed interpretation is the narrowing of the functional component of business processes to the resulting feature in real time.

2. Low indicators of network readiness, level of ICT development, global competitiveness of the domestic economy and business profitability have been established.

3. For the simulated situations, the results obtained allowed to bring the convergence of the resulting indicator of relatively independent factors, that is, the response of domestic business to the intensification of digitalization, increasing the competitiveness of the economy and the development of information and communication technologies.

4. The paper proposes an algorithm for applying the methodology for assessing the functioning of a business using neural network technologies.


Ключові слова


algorithm, methodology, business process, result, efficiency, neural network modeling

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DOI: https://doi.org/10.35774/sf2019.03.108

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