SUPERVISED MACHINE LEARNING FOR BANKRUPTCY PREDICTION: A BIBLIOMETRIC STUDY OF 2002 TO 2022 - Syafiza Saila Samsudin; Asmahani Nayan; Shahida Farhan Zakaria; Fazillah Bosli; Amirah Hazwani Abdul Rahim; Mohd Rijal Ilias, (ADVANCED INTERNATIONAL JOURNAL OF BUSINESS, ENTREPRENEURSHIP AND SME'S (AIJBES), 2024)
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.