Tax Avoidance Detection Based on Machine Learning of Malaysian Government-Linked Companies
Rahayu Abdul Rahman1, Suraya Masrom2, Normah Omar3
1Rahayu Abdul Rahman, Department of Accountancy, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia.
2Suraya Masrom, Department of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia.
3Normah Omar, Accounting Research Institute, Universiti Teknologi MARA, Selangor, Malaysia.
Manuscript received on 11 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 02 November 2019 | PP: 535-541 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10830982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1083.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine learning has been widely used in solving the problem of prediction and classification. It is also beneficial in the problem of tax avoidance detection. This study presents the utilization of machine learning classification approach for detecting tax avoidance of Malaysian government-linked companies (GLCs). There were nine machine learnings algorithms have been used on the real dataset collected from datastream and companies annual reports. The performance of these algorithms have been observed based on different training approaches and different features selection. The findings have revealed that the accuracy of results from each machine learnings were divergent according to the training approaches and features selection.
Keywords: Government-Linked Companies, Machine Learning, Prediction, Real Dataset, Tax Avoidance.
Scope of the Article: Machine Learning