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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 (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 |Pages: 14
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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Data Description

This bank marketing dataset includes 45211 observations, each with 20 attributes. The target y attribute shows the result if clients will subscribe the term deposit or not. The target feature is coded as “yes” to indicate client will subscribe a term deposit while “no” means client will not subscribe the term deposit. The 20 attributes are described below:

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