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

•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 be...

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Vydané v:Expert systems with applications Ročník 42; číslo 3; s. 1314 - 1324
Hlavní autori: Moro, Sérgio, Cortez, Paulo, Rita, Paulo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier Ltd 15.02.2015
Elsevier
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ISSN:0957-4174, 1873-6793
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Abstract •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.
AbstractList 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.
•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.
Author Rita, Paulo
Cortez, Paulo
Moro, Sérgio
Author_xml – sequence: 1
  givenname: Sérgio
  surname: Moro
  fullname: Moro, Sérgio
  email: scmoro@gmail.com
  organization: Business Research Unit (UNIDE-IUL), Department of Information Science and Technology, ISCTE – University Institute of Lisbon, 1649-026 Lisbon, Portugal
– sequence: 2
  givenname: Paulo
  surname: Cortez
  fullname: Cortez, Paulo
  email: pcortez@dsi.uminho.pt
  organization: ALGORITMI Research Centre, Department of Information Systems, University of Minho, 4800-058 Guimarães, Portugal
– sequence: 3
  givenname: Paulo
  surname: Rita
  fullname: Rita, Paulo
  email: paulo.rita@iscte.pt
  organization: Business Research Unit (BRU-UNIDE), ISCTE – University Institute of Lisbon, 1649-026 Lisbon, Portugal
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Issue 3
Keywords 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
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Snippet •A recent review on the application of business intelligence to the banking domain.•Coverage of the last twelve years of scientific literature on those...
This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in...
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SubjectTerms 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
Title Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation
URI https://dx.doi.org/10.1016/j.eswa.2014.09.024
https://www.proquest.com/docview/1660088596
Volume 42
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