企业税违约预测的特征转换:机器学习方法的应用

Mohammad Zoynul Abedin, M. Hassan, Md. Imran Khan, I. Julio
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引用次数: 2

摘要

机器学习(ML)和数据科学的应用已经显著扩展到当代会计和金融领域。然而,目前对纳税人身份的预测和分析相对尚未开发。此外,本文重点研究了特征变换作为企业税收状况预测的一个新研究领域与ML方法的适用性的结合。本文还应用了芬兰有限责任公司的纳税数据集,包括失败和非失败的税收信息。七种不同的机器学习方法在四个数据集上进行训练,从转换到非转换,有效地区分了非违约的税务公司和默认的税务公司。研究结果建议税务管理选择单一的最佳ML方法和特征转换方法来执行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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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