Machine learning In the financial industry: A bibliometric approach to evidencing applications

Abstract

AbstractThis study comprehensively reviews the key influential and intellectual aspects of machine learning in finance. The authors employ the bibliometric approach using VOSviewer software to analyse 189 academic articles from the SCOPUS database between 1988 and December 2022. Our results revealed that machine learning in the finance literature has significantly increased since 2017, indicating that the finance industry had some time to adopt newer technology. The authors find that the United States, China, and the United Kingdom were the countries that most frequently investigated this topic. It was also found that the Steven Institute of Technology (New Jersey, United States) is the most active research institute in this field. We also discovered that the application of machine learning has been adopted in crowdfunding, FinTech, forecasting, bankruptcy prediction, and computational finance. Our research is subject to several limitations. This research only utilised the SCOPUS database and was restricted to articles written in English. Our findings assist academic scholars in exploring issues related to machine learning in finance in future studies. The outcomes of the present study may also guide market participants, particularly FinTech and finance companies, on how machine learning could be used in their decision-making.

Keywords

• finance
• machine learning
• bibliometric
• VOS viewer
• SCOPUS database