Proactive VAT risk management system in the context of digitalization: problems, solutions and prospects

• Published in: Теоретическая и прикладная экономика no. 2; pp. 31 - 51
• Main Author: Balakin, Maksim Sergeevich
• Format: Journal Article
• Language: English
• Published: 01.02.2025
• ISSN: 2409-8647; 2409-8647
• DOI: 10.25136/2409-8647.2025.2.74485

The relevance of the study is due to the importance of developing and implementing pro-active tax administration systems capable of predicting and reducing tax risks for the state based on real data. The subject of the study is the system of VAT tax administration in the Russian Federation in the conditions of digitalisation and the risks associated with its functioning. The aim of the work is to substantiate the ways of improving VAT tax administration in the conditions of digitalisation through the formation of a proactive system of tax risk management of the state, based on the application of a scenario approach to the forecasting of tax revenues, considering the influence of relevant factors. The paper substantiates the proposals to create a more effective mechanism of VAT tax risk management based on forecasting models and scenarios. Special economic and mathematical methods were used for their construction: regression analysis, construction of autoregressive model with time lags and error correction models. To implement the proactive VAT risk management system, a software module – ASK VAT-2 – is proposed, integrating both strategic and operational decision-making. Its use will allow tax authorities to improve the automation of administration processes, reduce the cost of audits and focus on companies with a high level of tax risks. The distinctive feature of the proposed approach is that the system developed on its basis can predict VAT tax risks and taking appropriate preventive measures. The introduction of this system will contribute to the improvement of tax risk management of the state, increase the efficiency of the tax authorities and ensure more stable tax revenues to the budget. The direction of further research is to build models of differentiated groups of similar enterprises and individual large taxpayers using big data and artificial intelligence.