A Bibliometric Analysis of Corporate Bankruptcy Prediction Research: Trends and Insights

Abstract

This paper is the outcome of a bibliometric study aimed at providing a worldwide overview of the prediction of corporate bankruptcy. This research paper provides a thorough examination of bibliometric analysis in the field of corporate bankruptcy prediction, specifically emphasising Artificial Intelligence-based models. This study uses rigorous bibliometric methods to identify and analyse the journals with the highest productivity, the papers with the most citations, and the authors with the most citations in this particular field. In addition, the research environment is illuminated by descriptive analysis of authors, countries, journals, and keywords. The study also examines the expansion and patterns in publications, offering significant insights for identifying periods of heightened interest and possible domains for additional investigation. In addition, this study examines country-specific annotations and analyzes individual and country-specific co-authorship to reveal collaboration networks and research contributions in various locations. Co-citation analysis and co-occurrence analysis of journals provide a more profound understanding of how knowledge is spread and the formation of topic clusters within the field. The results of this study have important consequences for research institutions and politicians, providing valuable insights that can lead to future research and influence policy decisions in the field of corporate bankruptcy prediction. It will guide future research and advance the discourse on financial stability and risk management.