Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Applying Convolutional-GRU for Term Deposit Likelihood Prediction

Version 1 : Received: 5 July 2020 / Approved: 6 July 2020 / Online: 6 July 2020 (09:13:34 CEST)

How to cite: Dutta, S.; Bandyopadhyay, S. Applying Convolutional-GRU for Term Deposit Likelihood Prediction. Preprints 2020, 2020070101 (doi: 10.20944/preprints202007.0101.v1). Dutta, S.; Bandyopadhyay, S. Applying Convolutional-GRU for Term Deposit Likelihood Prediction. Preprints 2020, 2020070101 (doi: 10.20944/preprints202007.0101.v1).

Abstract

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits. For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis can influence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludes that proposed model attains an accuracy of 89.59% and MSE of 0.1041 which outperform well other baseline models.

Subject Areas

Term deposit subscription; Neural network; GRU; Convolutional layers; DT; MLP; k-NN

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