Artificial intelligence in financial services using bibliometric analysis

Abstract

Objective: The purpose this is to conduct a bibliometric analysis of financial services. This study reviews the publications from the past three decades and carries out performance analysis, co-citation analysis, bibliographic coupling and mapping. Methodology: The research investigates bibliometric analysis, performance evaluation and thematic clustering through Scopus database’s 393 documents. It focused on aspects such as the scientific output of articles, leading authors, influential papers, institutions and countries, keyword co-occurrence, thematic mapping, co-citations, as well as collaborations among the authors and nations. VOS-viewer was utilized as an instrument in this study to perform performance analysis and thematic clustering. Findings: The year 2024 saw the highest level of productivity with the total of 95 publication, while the University of Aberdeen, UK emerged as most notable institution and the country is “United Kingdom”. Additionally, the greatest influential & productive journal is “IEEE Access”. Moreover, the article that received the most citation is title “Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city (Singh et al., 2020) [34]”. The author also discovered five thematic clusters within the realm of artificial intelligence focus on financial services.