Journal article 166 views
Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
Abedin Abedin,
M. Kabir Hassan,
Imran Khan,
Ivan F. Julio
Asia-Pacific Journal of Operational Research, Volume: 39, Issue: 04
Swansea University Author: Abedin Abedin
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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...
| Published in: | Asia-Pacific Journal of Operational Research |
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| ISSN: | 0217-5959 1793-7019 |
| Published: |
World Scientific Pub Co Pte Ltd
2022
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa64276 |
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| 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. |
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| Keywords: |
Data mining, machine learning, default prediction, corporate tax |
| College: |
Faculty of Humanities and Social Sciences |
| Issue: |
04 |