Functional Model of Informational Support of Control and Supervisiory Activites of Federal Treasury

Today’s system of managing public finance demands from the Federal Treasury not only to register violations but also to prevent and forecast them. However, transit to pro-active risk-oriented model is hindered by a number of systemic problems: uncoordinated data, high operational load on employees and insufficient flexibility of control methods. The article put forward an approach to resolving these problems through elaboration of intellectual system of informational support of treasury work. Complex analysis of Federal Treasury functions were carried out and expanded classification of destructive factors was proposed, which was split into external (regulatory and legal problems, micro-economic risks and behavior of budget process participants) and internal ones (organization – managerial, technological restrictions, personnel and methodological problems). To appraise the total impact of these factors the integral indicators – index of treasury control (IEKK) was worked out. Its estimation is based on sigmoidal function, which allows us to take into account nonlinear nature of managerial process: improvement of problematic factor provides a bigger growth in efficiency than upgrading of favorable trend. The key result of the research was a functional model of informational support presented as an oriented graph. It describes the movement of multi-channel data flows from the multitude of external sources through nodes of verification, analysis and decision-making to end customers. The model helps mathematically formalize the impact of destructive factors: external ones distort the in-put data (additive error) and internal ones slow down treatment in nodes (temporary delays). To calibrate these parameters it was proposed to use digital double of control processes. These solutions offer applied value: they form the foundation for projecting the real intellectual informational system of the Treasury of Russia that could cut the load on employees, minimize ‘blind zones’ in control and provide transition to controlled budget risks on the basis of big data and predictive analysis.

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