Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation

• Published in: Expert systems with applications Vol. 42; no. 3; pp. 1314 - 1324
• Main Authors: Moro, Sérgio, Cortez, Paulo, Rita, Paulo
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.02.2015; Elsevier
• Subjects: Allocations; Applied sciences; Artificial intelligence; Banking; Business intelligence; Computer science; control theory; systems; Data mining; Data processing. List processing. Character string processing; Decision support systems; Dirichlet problem; Exact sciences and technology; Information management; Information systems. Data bases; Memory organisation. Data processing; Operational research and scientific management; Operational research. Management science; Portfolio theory; Searching; Software; Speech and sound recognition and synthesis. Linguistics; Text mining; Texts; Trends; Text mining; Decision support systems; Business intelligence; Data mining; Banking; Dictionaries; Targeting; Volatility; Fraud; Modeling; Technology watch; Business process; Cheating; Credit; Linguistics; Competitive intelligence; Natural language; Document analysis; Bankruptcy; Data analysis; Decision support system; Grouping; Text; Risk analysis; Document retrieval; Bank(institution); Supplier customer relationship
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2014.09.024

•A recent review on the application of business intelligence to the banking domain.•Coverage of the last twelve years of scientific literature on those subjects.•Usage of text mining and the latent Dirichlet allocation to analyze articles.•Provide new insights and future research trends which may benefit banking business. This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.