Optimizing Tax Preparation and Filing Services: A Comparative Study of Traditional Methods and AI Augmented Tax Compliance Frameworks

Abstract

Tax evasion has been defined as the illegal act that occurs when taxpayers deliberately choose to undermine their true state of individual or corporate finances, thus failing to accurately report their tax returns to the tax collector. This non-compliance behavior can be induced for the purpose of securing a discounted tax liability, or even, damaging and evading it entirely. On an annual basis, trillions of dollars in commercial and individual payments are lost due to tax evasion. To face this colossal loss, the government should execute enforcement strategies. Under this sense, tax service authorities have been alarmingly successful implementing both traditional and information-age. A real and unique tax environment of a European Union (EU) country is depicted, with the use of a gap analysis study. This is realized by planning and evaluating a SWOT analysis of the national revenue services against new automated machine-to-machine (M2M) tax collection competitor services. Upon successful completion of that, the design of an advanced state-of-the-art tax compliance framework is presented. The latter aims to initiate the building of an efficient automatic tax compliance environment for the BTC. Finally, a confirmation study is performed on the initial gap analysis, with a real data size that consists of a yearly interval of tax service filing logs, ranging from $5.9$ to $16.8$M actions.