Utilization of Artificial Intelligence in the Banking Sector: A Bibliometric Analysis

Banking sector, Bibliometric Analysis, Artificial Intelligence (AI)

This bibliometric analysis examines the research environment concerning the implementation of artificial intelligence (AI) in the banking sector from 2010 to 2026. The study concentrated on Scopus-indexed publications to recognize prominent research clusters. We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to evaluate the publications, resulting in 2790 that satisfied the inclusion criteria. The study found 257 words that were only used in the abstracts as well as the titles of the articles. Factor analysis identified five distinct clusters encompassing AI-fintech, risk assessment, cybersecurity, statistical modelling techniques, and marketing and technology adoption, among others. These clusters give a full picture of the complex world of research. The result highlighted two dimensions explaining 44.8% of the variance collectively and thus offers meaningful insights in the field. An evaluation of life cycle predictions shows that after a decade, the adoption of new technologies will change AI into something else. The evaluation also shows that cumulative growth will level off after 2030 and then start to drop from 2030 to 2050. After that, the subject matter score will keep going down, which indicates a shift from growth to saturation. The defined clusters provide significant direction for further inquiries and contribute to the development of research methodology. Future researchers, policymakers, academic community, and professionals in the banking sector can derive meaningful lessons from these findings, which underscore existing research deficiencies and emerging opportunities. This study enhances the bibliometric literature by providing innovative insights on AI-enabled advancements within the banking sector. Furthermore, the findings have substantial practical implications, facilitating the understanding of AI-driven technologies in the enhancement of academic pursuits and financial sector operations.