Mapping the Landscape of Artificial Intelligence in Banking: A Bibliometric Perspective

Abstract

Artificial intelligence (AI) technologies are transforming the banking sector, creating radical changes in operational efficiency, customer experience, and risk management. For an institution that thoroughly analyzes its own and its customers' needs and desires, this requires continuous transformation and adaptation to market conditions. Furthermore, the use of AI is undeniable in this process, which has evolved since the beginning of the 21st century and requires the analysis of big data. Machine learning and natural language processing (NLP)-based chatbots offer speed and personalization in customer service, while fraud detection systems increase the security of financial transactions with their anomaly detection capabilities. Furthermore, automating repetitive tasks through robotic process automation provides significant reductions in operational costs. Data privacy and security concerns are further complicated by legal regulations such as GDPR. Consequently, while AI plays an indispensable role in the banking sector, ethical, regulatory, and technological infrastructure issues also need to be addressed. This study analyzes the existing scientific literature on the role of artificial intelligence in the banking sector using bibliometric methods and evaluates the studies in this field. The dataset for the study was taken from the Web of Science (WoS) database, and a bibliometric analysis was conducted using Vosviewer. The analysis identified studies covering the period 1992-2025, totaling 247 publications. It concluded that scientific productivity was low between 1992-2018, but has accelerated and increased since 2018, with a significant increase in studies in this field in recent years. Furthermore, the importance of the concepts of "fintech," "credit risk," and "machine learning" for researchers was emphasized.