March 26, 2024Open Access

Supervised Machine Learning for Bankruptcy Prediction: A Bibliometric Study of 2002 to 2022

Key Points

Forecasting bankruptcy through supervised machine learning reveals critical trends over two decades.
The analysis covered 361 scholarly publications, indicating significant research growth and interest.
Assessment of publications utilized tools like VOSviewer and Publish and Perish to extract meaningful data insights for the field of bankruptcy prediction over the years. The findings underscore the importance of using advanced prediction techniques for preventing business failures and improving financial stability.

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.

Supervised Machine Learning for Bankruptcy Prediction: A Bibliometric Study of 2002 to 2022

Key Points

Abstract

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