Scientific Trend Analysis of Artificial Intelligence Applications in Banking Models using Text Mining Techniques
Keywords:
Banking models, Artificial Intelligence, Text mining, Classification, K-Nearest Neighbors, Box-JenkinsAbstract
Reviewing scientific articles and comparing their status can identify scientific gaps and potential opportunities. This study focuses on the field of hybrid models of banking and artificial intelligence (AI). AI applications in banking have grown significantly, ranging from fraud detection and risk assessment to personalized customer services and automated trading systems. These technologies are not only enhancing operational efficiency but also transforming how financial institutions interact with their customers and manage risks. In this paper, after extracting data from the Scopus database, categorization was performed on 4,795 reputable articles over the past 14 years (2010-2023). Clusters were created using text mining techniques to assign subject labels in the interdisciplinary fields of AI and banking. The Box-Jenkins approach was then used to select a model on the data and predict and analyze trends over different periods. The results indicate the primary focus areas for applying AI in banking are: Innovation, Technologies and Digital Banking (58.89%), Commercial and Investment Banking (27.13%), Retail, Personal and Wealth Management Banking (9.49%), and International and Global Operations Banking (4.48%).
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Copyright (c) 2024 Ramin Khoshchehreh Mohammadi (Author); Mehrdad Hosseini Shakib; mahmood khodam, Ali Ramezani (Author)
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