There are many studies that have applied
data mining to banking. However, the lack of proper data
mounts a serious obstacle to the employment of data
mining techniques by banks. This paper examines previous data mining research in the field of banking to extract all served entities and attributes required for analytical purposes, categorize these attributes and ultimately
present a data model for analysis. After analyzing a wide
range of data mining applications in banking, 28 entities
with 423 attributes were identified and the final proposed
entity-relationship model was drawn. Also, a checklist was
provided based on the model for auditing data gap in
banks and applied to a real case. The results of this
paper can be seen as a supportive tool for improving
bank‘s business intelligence maturity from the data perspective and enabling managers for analyzing data requirement of information systems.