Mapping the Research Landscape of AI and ML Applications in Financial Services: Insights from a Bibliometric Study

This study maps the global research landscape of Artificial Intelligence (AI) and Machine Learning (ML) applications in financial services through a comprehensive bibliometric analysis. It aims to identify prevailing themes, influential publications, collaborative networks, and emerging trends that are shaping the future of technology-driven financial services. The analysis is based on 140 scholarly documents—including journal articles, books, and conference papers—published in English between 2019 and 2025, and indexed in Scopus. These documents span diverse subject areas such as Computer Science, Engineering, Business, Management and Accounting, Economics, and Econometrics. The Bibliometrix package in R (via the Biblioshiny interface), VOSviewer, and Microsoft Excel, were used for performance analysis and science mapping techniques. VOSviewer was used to visualize keyword co-occurrence networks and thematic clusters. The findings highlight the growing significance of AI and ML in enhancing operational efficiency, improving decision-making, optimizing customer experiences, and strengthening risk management in financial services. The analysis also reveals research gaps and suggests directions for future studies, particularly in areas linking AI and ML applications with sustainable financial service models.This study contributes valuable insights for researchers, industry practitioners, and policymakers by offering a structured overview of the knowledge base and trends in this rapidly evolving field. It serves as a strategic reference point for guiding future research and fostering innovation in the financial services sector through AI and ML technologies.