Decreasing Tax Evasion by Artificial Intelligence

Abstract

Abstract The paper addresses an approach to combat corporate tax evasion by using the possibility of indirect accounting of latent factors in taxation processes. Value chains formed in the digital environment are increasingly falling out of government control, and digital platforms allow the building of relationships that are opaque for tax authorities. The digital market itself is becoming a factor that creates new value and possibilities. The sharing society is self-learning to reduce the tax base: business is getting rid of intermediaries, eliminating the regulator, and avoiding double taxation and taxation in general. This is happening against the backdrop of the chaotic development of the global financial system. The paper’s idea is based on creating a decision-making system for predicting tax evasion events by forming a special information framework of potentially suspicious events, using methods of deep learning of neural networks, and cognitive modelling. The latter was enriched with non-formalizable cognitive semantics, which increases the transparency of tax events. In real practice, the approach has helped to diagnose suspicious tax evasion cases during supplying products for the Arctic zone of Russia