SUPERVISED MACHINE LEARNING FOR BANKRUPTCY PREDICTION: A BIBLIOMETRIC STUDY OF 2002 TO 2022

Authors

  • Syafiza Saila Samsudin Department of Mathematics, Universiti Teknologi MARA, Malaysia
  • Asmahani Nayan Department of Mathematics, Universiti Teknologi MARA, Malaysia
  • Shahida Farhan Zakaria Department of Mathematics, Universiti Teknologi MARA, Malaysia
  • Fazillah Bosli Department of Mathematics, Universiti Teknologi MARA, Malaysia
  • Amirah Hazwani Abdul Rahim Department of Mathematics, Universiti Teknologi MARA, Malaysia
  • Mohd Rijal Ilias Department of Mathematics, Universiti Teknologi MARA, Malaysia

Abstract

The devastating after-effects of bankruptcy include the shutting down of business establishments and the dismissal of employees. This present study examines the use of supervised machine learning to predict bankruptcy between 2002 and 2022 by bibliometrically evaluating 361 scholarly publications from the Scopus database in September, 2022. VOSviewer, Harzing’s Publish and Perish, and Microsoft® Excel were used to analyse the data. The outcome of this bibliometric analysis provides a clearer and wider understanding of existing and future trends of using supervised machine learning to forecast bankruptcy.

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Published

2024-09-24

How to Cite

Syafiza Saila Samsudin, Asmahani Nayan, Shahida Farhan Zakaria, Fazillah Bosli, Amirah Hazwani Abdul Rahim, & Mohd Rijal Ilias. (2024). SUPERVISED MACHINE LEARNING FOR BANKRUPTCY PREDICTION: A BIBLIOMETRIC STUDY OF 2002 TO 2022. ADVANCED INTERNATIONAL JOURNAL OF BUSINESS, ENTREPRENEURSHIP AND SME’S (AIJBES), 6(19). Retrieved from https://gaexcellence.com/aijbes/article/view/4106

Issue

Vol. 6 No. 19 (2024): Volume: 6 Issues: 19 [March, 2024]

Section

Articles