Modeling the Probability of the Detection Process of Tax Evasion Taking into Account Quality and Quantity Indicators

Aims: 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 taking into consideration qualitative and quantitative indicators.

Study Design: 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.

Place and Duration of Study: Department of Mathematical provision of economic researches, between November 2019 and May 2020.

Methodology: For the evaluating of the probability of tax evasion’s detection firstly, efficiency indicators of tax administration were selected including 3 groups such as internal environmental, external micro, and external macro-environmental factors. These indicators consist of both quantitative as well as qualitative indicators. Quantitative indicators were assessed on the base on statistics information base. Quantitative indicators were assessed on the base of expert skills, knowledge, and experiences in accordance with under investigation countries. The objectiveness of obtained data that characterize qualitative indicators was checked and used both these as well as quantitative indicators for formulating the tax efficiency index. The next step is consists of using these formulations for evaluating the probability of detection of tax evasion under uncertainty. The impact degrees (membership functions) of the parameters that characterize the influence of 3 groups-environmental factors, in the detection of tax evasion were defined, and taking them into account in the fuzzy inference system probability of detection of tax evasion was assessed.

Limitations: Lack or uncertainties of the information base cause difficulties in applying our model.

Results: The probability of detection of tax evasion in the Republic of Azerbaijan was assessed with the proposed model and depends on the results recommendations have been consulted for improving appropriate tax system. As a result of model the probability of detection of tax evasion was defined 29%. The result shows that tax administration mechanism in Azerbaijan Republic need to be improved.

Conclusion: 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.