Bank Marketing Data Mining

Abstract

A lot of businesses include banks has use direct marketing strategies to reach customers in order to maximize the return rate and minimize the campaigning cost. In the bank marketing, the application of data mining classification is essential to determine the valuable customer and support the effective usage of Customer Relationship Management (CRM) system. And the unbalanced data mostly affect the accuracy rate of the results. Therefore, this study determine the preferred classification model based on the accuracy ration and other classification matrices after convert it to SMOTE "balanced", for enhancing the efficiency of bank marketing. The obtained results demonstrated RANDOM FOREST (RF) with MultiBoostAB recorded highest accuracy rate of 95.2996 from, which can be applied for direct marketing application.