Bank direct marketing analysis of asymmetric information based on machine learning

The bank direct marketing campaign for offering products that meet the customers' needs is the challenge problems. The bank direct marketing data analysis is important work that helps the banks predict whether customers will sign a long term deposits with the banks. The method that can predict such customers' needs can be profitable to the banks for improving their marketing campaign strategies. Unfortunately, it is very hard to predict the customers' needs because the available information is asymmetric. In this paper, we propose the method to analyze asymmetric information using SMOTE algorithm and Rotation Forest (PCA)-J48. The SMOTE method is used to modify the data and improve the accuracy of the prediction. The performance of the proposed method is evaluated and compared to Decision Tree, Rotation Forest, Navie Bayes, BayesNet, Multilayer Perceptron Neural Network, RBF Neural Network. The experimental results show the predicting accuracies of all predictors. The experiments show that Rotation Forest (PCA)- J48 can achieve the highest value of accuracy and specificity. However, the sensitivity of Rotation Forest (PCA)-J48 is higher than all methods except BayesNet and Rotation Forest (PCA) RandomTree.