@article {772,
	title = {Data Mining Applications for Banks{\textquoteright} Business Intelligence Maturity-},
	journal = {Journal of Digital Information Management},
	volume = {18},
	year = {2020},
	month = {10/2020,12/2020},
	pages = {153-172},
	type = {Research},
	chapter = {153},
	abstract = {<p><span style="color: rgb(51, 51, 51); font-family: Arial, Helvetica, sans-serif;">There are many researches that applied data mining in banking. However, one may be countered the lack of suitable data as a serious obstacle to employ data mining techniques for the banks. This paper examines previous data mining researches carried out for banking, integrates them, extracts all served entities and attributes which are needed for analytical purposes, categorizes them and ultimately presents a suitable data model for analytical purpose. After analysis of a wide range of data mining application in banking, 28 entities with 423 attributes were identified in conclusion and the final proposed entity-relationship diagram was created. Also a checklist was provided based on the model for auditing data gap in banks and applied in a real case. The results of this paper can be regarded as a supportive tool for increasing banks{\^a}{\texteuro}{\texttrademark} business intelligence maturity from the data perspective and enable managers in requirement analysis of information systems.</span></p>
},
	doi = {https://doi.org/10.6025/jdim/2020/18/5-6/163-172},
	url = {http://www.dline.info/download.php?sn=3103},
	author = {Mina Ranjbarfard and Shahideh Ahmadi}
}
