Predicting the success of bank telemarketing using deep convolutional neural network

Recently, exploitations of the financial big data to solve the real world problems have been to the fore. Deep neural networks are one of the famous machine learning classifiers as their automatic feature extractions are useful, and even more, their performance is impressive in practical problems. Deep convolutional neural network, one of the promising deep neural networks, can handle the local relationship between their nodes which can make this model powerful in the area of image and speech recognition. In this paper, we propose the deep convolutional neural network architecture that predicts whether a given customer is proper for bank telemarketing or not. The number of layers, learning rate, initial value of nodes, and other parameters that should be set to construct deep convolutional neural network are analyzed and proposed. To validate the proposed model, we use the bank marketing data of 45,211 phone calls collected during 30 months, and attain 76.70% of accuracy which outperforms other conventional classifiers.