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Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription

Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription

Sipu Hou, Zongzhen Cai, Jiming Wu, Hongwei Du, Peng Xie
Copyright: © 2022 |Volume: 9 |Issue: 1 |Pages: 14
ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781683182870|DOI: 10.4018/IJBAN.288514
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MLA

Hou, Sipu, et al. "Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription." IJBAN vol.9, no.1 2022: pp.1-14. http://doi.org/10.4018/IJBAN.288514

APA

Hou, S., Cai, Z., Wu, J., Du, H., & Xie, P. (2022). Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription. International Journal of Business Analytics (IJBAN), 9(1), 1-14. http://doi.org/10.4018/IJBAN.288514

Chicago

Hou, Sipu, et al. "Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription," International Journal of Business Analytics (IJBAN) 9, no.1: 1-14. http://doi.org/10.4018/IJBAN.288514

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Abstract

It is not easy for banks to sell their term-deposit products to new clients because many factors will affect customers’ purchasing decision and because banks may have difficulties to identify their target customers. To address this issue, we use different supervised machine learning algorithms to predict if a customer will subscribe a bank term deposit and then compare the performance of these prediction models. Specifically, the current paper employs these five algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Neural Network. This paper thus contributes to the artificial intelligence and Big Data field with an important evidence of the best performed model for predicting bank term deposit subscription.