| Abstract: |
With the expansion of epidemic diseases and after the crises of the economy in the world, choosing financial deposits for many purposes is very helpful. To identify a customer whether deposit or not, based on the information given to analyze and predict, it is becoming increasingly difficult for banks to identify whether customer that is right for them. Many banks will be reconfigured beyond recognition to attract customers, while others are facing a shortage drawing customers to maintain the business as a corollary of advances in particular. To serve customers with the information needed to select a suitable deposit in such a rapidly evolving and competitive arena requires more than merely following one’s passion. We assert such information may be derived by analyzing some descriptions using deep neural network models, a novel approach to identifying the descriptions about age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaigns, pdays, previous, outcome, deposit (y) in choosing an appropriate deposit customer. There have been some researchers written about this prediction but they just focused on algorithms models instead of concentrating on deep machine learning. In this paper, we will muster up algorithms using the models on deep machine learning with Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long-Short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Simple Recurrent Neuron Network (SimpleRNN). The result will suggest suitable customers based on the information given. The results showed that Gated Recurrent Unit (GRU) reaches the best accuracy with 90.08% at epoch 50th, and the following is the Bidirectional Long-Short Term Memory (BiLSTM) model with 90.05% at epoch 50th. The results will be helpful for the banks to confirm whether the customers could deposit or not. |