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Acta Scientific | International Open Library | Open Access Journals Publishing Group

Acta Scientific Computer Sciences (ASCS)

Research Article Volume 2 Issue 10

Modeling the Detection Process of Tax Evasion Under Uncertainty

Musayev Akif Farhad1* and Mirvari Gazanfarli2

1Azerbaijan University, Baku, Azerbaijan Republic
2Department of Mathematical Provision of Economic Research, ANAS Institute of Economy, Baku, Azerbaijan Republic

*Corresponding Author: Musayev Akif Farhad, Professor, Director, Department of Mathematical Provision of Economic Research, ANAS Institute of Economy, Azerbaijan University, Baku, Azerbaijan Republic.

Received: July 20, 2020; Published: September 08, 2020

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Abstract

In contrast to the classical approaches of the standard model of tax evasion based on game theory, our manuscript has considered the detection of tax evasion as one of the main function of tax administration and has proposed a model for assessing the probability of tax evasion. This investigation has been carried out on the basis of research methods such as scientific abstraction and systematic analysis, expert evaluation, logical generalization, statistical analysis. The level of tax administration, as well as the probability of detection of tax evasion in the Republic of Azerbaijan has been assessed with the proposed model and depend on the results, recommendations have been consulted for improving appropriate tax system. Proposed model drives practical significance as a providing effective activity of tax institutions by defining the level of tax administration, as well as, as an impacting remarkably the revenue of state budget by determining the probability of tax evasion's detection.

Keywords: Probability of Detection of Tax Evasion; Tax Administration Efficiency Index; Allingham-Sandmo Model; Expert Evaluation; Uncertainty; Fuzzy Inference System

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References

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Citation

Citation: Musayev Akif Farhad and Mirvari Gazanfarli. “Modeling the Detection Process of Tax Evasion Under Uncertainty”.Acta Scientific Computer Sciences 2.10 (2020): 03-08.



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