Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches

Mohammad Zoynul Abedin (), M. Kabir Hassan, Imran Khan () and Ivan F. Julio ()
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Mohammad Zoynul Abedin: Department of Finance and Banking, Hajee Mohammad Danesh Science and Technology, University, Dinajpur, Bangladesh
Imran Khan: Department of Computer Science and Engineering, Gono Bishwabidyalay, Bangladesh
Ivan F. Julio: Department of Administrative Sciences, Metropolitan College, Boston University, 1010 Commonwealth, Ave, Room 428, Boston, MA 02215, USA

Asia-Pacific Journal of Operational Research (APJOR), 2022, vol. 39, issue 04, 1-26

Abstract: Applications of machine learning (ML) and data science have extended significantly into contemporary accounting and finance. Yet, the prediction and analysis of taxpayers’ status are relatively untapped to date. Moreover, this paper focuses on the combination of feature transformation as a novel domain of research for corporate firms’ tax status prediction with the applicability of ML approaches. The paper also applies a tax payment dataset of Finish limited liability firms with failed and non-failed tax information. Seven different ML approaches train across four datasets, transformed to non-transformed, that effectively discriminate the non-default tax firms from their default counterparts. The findings advocate tax administration to choose the single best ML approach and feature transformation method for the execution purpose.

Keywords: Data mining; machine learning; default prediction; corporate tax (search for similar items in EconPapers)
JEL-codes: C12 C44 C45 H26 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0217595921400170

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