Design and Implementation of an Efficient Electronic Bank Management Information System Based Data Warehouse and Data Mining Processing

• DW provides a versatile solution to the user, who can explore database effectively. • User doesn't need to know about relational model or sophisticated query languages. • This data analysis method allows OLTP systems to be optimized for data analysis. The quantity of electronic bank data grows exponentially with development of Information Technology (IT). The size of these data is impossible for traditional database and human analyst to come up with interesting information that will help in process of decision making. Management Information System (MIS) based Data warehouse (DW) and Data Mining (DM) techniques support the development of IT and process of management decision-making. But the traditional DW size make the query complex, which may cause unacceptable delay in decision support queries. Thus, in this paper an Efficient Electronic Bank MIS based DW and Mining Processing (EEBMIS-DWMP) was developed with cluster and non-cluster indexed view to provide decision-makers with both best response time and precise information. Also, analysis of the multilayer perception neural network, naïve Bayes, random forest, logistic regression, support vector machine and C5.0 on a real-world data of bank was done to improve effectiveness for campaign by analyzing the most useful features that influence campaign success. Results offer how the proposed EEBMIS-DWMP developed bank organizations by comparing performance of system with and without index view in terms of balance accuracy, accuracy, precision, recall, mean absolute error, root mean square error, F measure and running time. Conclusions from results offers that EEBMIS-DWMP can construct a database for each customer, a storage system that integrates data from a variety of sources into a single unified framework, decrease errors and time required to prepare financial reports, quickly access for information, analysis of data in multivariate, accurate prediction of competent, profitability segmentation.