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Umran, Fankar
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Humanities Law, Crime, Criminology & Criminal Justice Social Sciences

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Artificial Intelligence in Efficient and Effective Tax Audit Alam, Ryan Saputra; Umran, Fankar; Saleh, Hendra Noor
Journal of Tax Law and Policy Vol. 3 No. 1 (2024): Journal of Tax Law and Policy
Publisher : Scientia Integritas Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56282/jtlp.v3i1.536

Abstract

Tax ratio to Gross Domestic Product (GDP) in Indonesia in the last 5 (five) years is still below 11%. In fact, the standards of the United Nations, International Monetary Fund, World Bank and the Organization for Economic Co-operation and Development in 2015 have agreed that the tax ratio to achieve the Sustainable Development Goals should be at least 15%. Based on the conceptual review, this study produces 2 (two) conclusions. First, the implementation of tax audits has not made a significant contribution to increasing the tax ratio in Indonesia. Tax audits that contribute to improving tax compliance and increasing the tax ratio should be supported by technology that can increase the effectiveness and efficiency of the tax audit itself. Second, Tax Auditors and AIs should collaborate in handling routine tax audit tasks. It is recommended that the DGT seek to increase its tax auditors' interest in AI technology in improving audit evidence collection and that tax auditors should be able to rely on the use of intelligent software in improving the quality of the audit process..
Co-Authors Alam, Ryan Saputra Saleh, Hendra Noor