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Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription
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Author(s): Sipu Hou (California State University, East Bay, USA), Zongzhen Cai (California State University, East Bay, USA), Jiming Wu (California State University, East Bay, USA), Hongwei Du (California State University, East Bay, USA)and Peng Xie (California State University, East Bay, USA)
Copyright: 2022
Volume: 9
Issue: 1
Pages: 14
Source title:
International Journal of Business Analytics (IJBAN)
Editor(s)-in-Chief: John Wang (Montclair State University, USA)
DOI: 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.
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