Neural network approaches to tax risk identification: the concept of a “Tax neural shield”

In the context of the rapid digitalization of public administration and the increasing complexity of the fiscal environment, there is an increasing need to move from regulatory and legal mechanisms of tax administration to intellectually oriented models based on artificial intelligence technologies. The relevance of the study is determined by the need to increase fiscal efficiency, reduce tax losses and strengthen confidence in the tax system while respecting the principles of legality and personal data protection. The purpose of this article is the theoretical substantiation and applied modeling of the author’s concept of the “Tax Neural Shield”, an intelligent digital platform of a new generation for tax risk management. The methodological basis of the study was system analysis, machine learning methods, architectural design of neural network solutions, as well as regulatory analysis of fiscal policy. The paper presents the functional and architectural features of the proposed model, including adaptability, modularity, integration of multi-source data, algorithmic transparency and the possibilities of legal audit. Based on the GAP analysis, the current barriers to the introduction of AI into the tax practice of the Russian Federation are demonstrated and the institutional need for the intellectualization of tax control is substantiated. The results obtained indicate the potential of the “Tax Neuro Shield” as a tool not only to increase tax collection and reduce corruption risks, but also to create a trusting digital environment for fiscal interaction. The presented concept is considered by the author as an innovative vector of development of tax administration, focused on sustainability, transparency and predictivity in the digital economy.