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

Mohammad Abedin Orcid Logo, M. Kabir Hassan, Imran Khan, Ivan F. Julio

Asia-Pacific Journal of Operational Research, Volume: 39, Issue: 04

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1142/s0217595921400170

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 dom...

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Published in: Asia-Pacific Journal of Operational Research
ISSN: 0217-5959 1793-7019
Published: World Scientific Pub Co Pte Ltd 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa64276
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
College: Faculty of Humanities and Social Sciences
Issue: 04

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