Abstract
The graph model for electronic money turnover developed in this paper considers the system of electronic money turnover as a technological complex network. This network includes systems of electronic money payments, communications between bank and its clients, and interbank communications. The application of the graph models is based on its essential advantages such as an opportunity to expand this system to arbitrary size and visualization of the system links. While graph plotting provides us with the opportunity of carrying out qualitative (visual) system analysis, e computations of the graph metric allows performing a more quantitative analysis. The composite metric, created on the base of graph centrality measures and giving us possibilities of estimating and ranking potential risks, is considered as a foundation for methods of stability, quality and economic security control for systems of the electronic money turnover. A validity of this classification has been investigated and supported by the so-called crash tests, which simulate the random consecutive deleting of graph nodes represented in the real life by communication network nodes, for example, banks or other members of electronic money turnover system, and also by the analysis of the overall performance of the system.
Highlights
Contemporary processes of digital economy development determine global communication outspread for systems of electronic money turnover
The paper deals with an application of graph analytics for simulation of electronic money turnover systems
The digital networks of electronic money turnover can be analysed with the help of complex networks theory because such systems possess typical features of complex network – the nodes in these networks are nonequivalent, there is a small number of nodes with great amount of links
Summary
Contemporary processes of digital economy development determine global communication outspread for systems of electronic money turnover. The graph models are actively developed recently for investigation of internet communities of different kinds, but application of such type models to electronic money turnover analysis is not quite obvious. The description and analysis of such network can’t be carried out by means of tools developed for classical random networks with fixed numbers of nodes and links or by means of tools created for crystal lattice description. All these means that the new approaches should be developed for such network study, and simulation by means of graph analytics turns out to be possible and effective tools for these purposes. The idea of graph analysis based on Big Data is discussed and elaborated in different spheres of finance
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