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

telligence 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 or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence 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 proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. 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 ar- ticles 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

Information systems continuance intention of web-based applications customers : the case of online banking

The proliferation of the Internet has not only allowed businesses to offer their products and services through web-based applications, but it has also undermined their ability to retain their customers. It has reduced search costs, opened up barriers to entry, and diminished distinctiveness of firms. Effective retention of customers allows firms to grow in size and popularity, thereby increasing their profitability. We extended Commitment–Trust theory, an expectation–confirmation model, and technology acceptance theory to develop a model of IS continuance intention of customers of web-based applications. Relationship commitment and trust were found to be central to IS continuance intention. Also, perceived empowerment influenced relationship commitment, while perceived security influenced trust. Our findings thus supported traditional intention factors, highlighting the role of trust as a stronger predictor of intention than commitment but, contradicting findings from marketing research, trust was found to be a stronger predictor of retention in the e-commerce context