Applying Convolutional-GRU for Term Deposit Likelihood Prediction

Authors

  • Shawni Dutta Assistant Professor, Department of Computer Science, The Bhawanipur Education Society College, Kolkata, INDIA
  • Payal Bose Research Scholar, GLA University, Mathura-Delhi Road, Mathura, Chaumuhan, Uttar Pradesh, INDIA
  • Vishal Goyal Professor, Department of Computer Science, GLA University, Mathura-Delhi Road, Mathura, Uttar Pradesh, INDIA
  • Samir Kumar Bandyopadhyay Professor, Department of Computer Science, The Bhawanipur Education Society College, Kolkata, INDIA

DOI:

https://doi.org/10.31033/ijemr.11.3.42

Keywords:

Term Deposit Subscription, Neural Network, GRU, Convolutional Layers, DT, MLP, k-NN

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 caninfluence 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 concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.

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Published

2021-06-30

How to Cite

Shawni Dutta, Payal Bose, Vishal Goyal, & Samir Kumar Bandyopadhyay. (2021). Applying Convolutional-GRU for Term Deposit Likelihood Prediction. International Journal of Engineering and Management Research, 11(3), 265–272. https://doi.org/10.31033/ijemr.11.3.42

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Articles