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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
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last_indexed 2024-11-25T14:13:46Z
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spelling 2023-09-19T16:08:17.0862963 v2 64276 2023-08-31 Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE 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. Journal Article Asia-Pacific Journal of Operational Research 39 04 World Scientific Pub Co Pte Ltd 0217-5959 1793-7019 Data mining, machine learning, default prediction, corporate tax 1 8 2022 2022-08-01 10.1142/s0217595921400170 http://dx.doi.org/10.1142/s0217595921400170 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2023-09-19T16:08:17.0862963 2023-08-31T19:09:18.4863021 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Mohammad Abedin 0000-0002-4688-0619 1 M. Kabir Hassan 2 Imran Khan 3 Ivan F. Julio 4
title Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
spellingShingle Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
Mohammad Abedin
title_short Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
title_full Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
title_fullStr Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
title_full_unstemmed Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
title_sort Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Mohammad Abedin
M. Kabir Hassan
Imran Khan
Ivan F. Julio
format Journal article
container_title Asia-Pacific Journal of Operational Research
container_volume 39
container_issue 04
publishDate 2022
institution Swansea University
issn 0217-5959
1793-7019
doi_str_mv 10.1142/s0217595921400170
publisher World Scientific Pub Co Pte Ltd
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1142/s0217595921400170
document_store_str 0
active_str 0
description 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.
published_date 2022-08-01T20:24:30Z
_version_ 1821347855363735552
score 11.04748

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