Tax Compliance: Development of Artificial Intelligence on Tax Issues

Abstract

The development of robotic technology, especially AI, is able to forecast and present statistical analysis for almost all purposes, including taxes. This study aims to explore the various breakthroughs presented that helped managers and policymakers determine alternatives to improve corporate tax compliance by the qualitative method. The results of this study showed that the implementation of AI has been adapted quite a lot, but it is still not enough, especially in measuring the right tax compliance model for the manufacturing industry, which is the largest tax contributor in Indonesia. AI is said to help managers make more complex judgments with simulated risks. Early detection of tax fraud, leakage of year-end tax shocks, and misuse of tax costs can be minimized by AI optimization. However, there is a discussion about this AI threat on the security aspect that needs to be understood, so that the AI optimization strategy in taxes can mitigate the level of tax audit risk. The recommendation for next study is the exploration of ultimate expectations of corporate tax compliance are apparent.


Keywords: artificial intelligence, development, tax issues, digitalization, manufacture

References
[1] Cliff D, Terrence H, Oliver L, Jean-Pierre Z. The future of computer trading in the financial markets. University of Bristol

[2] Zhou L. Opportunities and challenges of artificial intelligence in the application of taxation system. In 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019) 2019 Dec 20 (pp. 201-206). Atlantis Press.

[3] Roger Institute Artificial Intelligence Research Group. Application of artificial intelligence technology in tax collection and management. International Taxation. 2018;5:20-24.

[4] Wu HD. Institutional arrangement and legal regulation in the age of artificial intelligence. Social Science Abstracts. 2017;12:76-78.

[5] Asatryan Z, Joulfaian D. Taxes and business philanthropy in Armenia. Journal of Economic Behavior & Organization. 2022 Aug 1;200:914-930.

[6] Savić M, Atanasijević J, Jakovetić D, Krejić N. Tax evasion risk management using a hybrid unsupervised outlier detection method. Expert Systems with Applications. 2022 May 1;193:116409.

[7] Zhang X, Chan FT, Yan C, Bose I. Towards risk-aware artificial intelligence and machine learning systems: An overview. Decision Support Systems. 2022 May 2:113800.