Template-Type: ReDIF-Article 1.0
Author-Name: Ayoub LOUHAB
Author-Email: louhab.ayoub1994@gmail.com
Author-Workplace-Name: Faculty of Legal, Economic and Social Sciences of ain sebaa, Hassan II University of Casablanca, Morocco
Author-Name: Abdelhak YAACOUBI
Author-Email: abdelhak.yaacoubi@etu.univh2c.ma
Author-Workplace-Name: Faculty of Legal, Economic and Social Sciences of ain sebaa, Hassan II University of Casablanca, Morocco
Author-Name: Mohamed AZOUAZI
Author-Email: azouazii@gmail.com
Author-Workplace-Name: Faculty of Sciences Ben M'sick, Hassan II University of Casablanca, Morocco
Title: Mapping the Evolution of Artificial Intelligence in Banking: A Systematic Bibliometric Review of Research Trends, Techniques, Applications, and Emerging Challenges
Abstract: Artificial intelligence (AI) development has revolutionized the financial industry, providing solutions for better fraud detection, credit risk assessment, customer relationship management, bank operations and regulation. Despite numerous publications dedicated to the topic in question, there are no bibliometric studies devoted to exploring the development, structure of knowledge bases and trends in research on AI applications in banking. Therefore, to fill the gap, this paper provides a systematic review of the existing literature concerning the application of AI in banking based on a dataset of 114 papers indexed in Scopus from 2020 to 2025. The systematic approach is applied to the research based on the methodology described in the PRISMA guidelines and such analytical tools as Bibliometrix, Biblioshiny and VOSviewer are used to assess the scientific output, key sources of information, Institutions and countries and thematic links via keyword co-occurrences. Thus, the annual increase rate of publication number in this area is estimated at 39.77% and involves 96 publication sources and 394 authors. Moreover, six topical issues related to the field under investigation are revealed, including fraud detection using machine learning techniques, credit and risk management, digital banking, cybersecurity, customer-focused applications and explainable AI. The overall findings from the findings indicate that the research is moving from the stage of developing individual algorithms to building a holistic approach towards developing intelligent banking systems that can be regulated. Through integrating bibliometric analysis with an extensive review on the use of AI in banking, this paper adds to the existing body of knowledge and identifies new areas for research in explainable AI, data governance, ethical implementation, and intelligent financial services. JEL Classification: G21, G28, O33 Paper type: Theoretical Research and Empirical Research
Keywords: Artificial Intelligence; Banking; Machine Learning; Fraud Detection; Risk Management; Bibliometric Analysis; VOSviewer; Bibliometrix
Journal: International Journal of Accounting, Finance, Auditing, Management and Economics
Pages: 415-437
Volume: 7
Issue: 8
Year: 2026
File-URL: https://ijafame.org/index.php/ijafame/article/view/2523
File-Format: text/html
File-URL: https://ijafame.org/index.php/ijafame/article/view/2523/2589
File-Format: application/pdf
Handle: RePEc:aoa:journl:v:7:y:2026:i:8:p:415-437