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

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...

Full description

Saved in:
Bibliographic Details
Published in:Asia-Pacific journal of operational research 2022-08, Vol.39 (4)
Main Authors: Abedin, Mohammad Zoynul, Hassan, M. Kabir, Khan, Imran, Julio, Ivan F.
Format: Article
Language:English
Subjects:
Corporate taxes
Datasets
Default
Machine learning
Operations research
Transformations (mathematics)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0217-5959
1793-7019
0217-5959
DOI:10.1142/S0217595921400170