Applying Machine Learning to the Development of Prediction Models for Bank Deposit Subscription
Year: 2021
Type: article
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.
DOI: 10.4018/ijban.288514
Authors: Sipu Hou, Zongzhen Cai, Jiming Wu, Hongwei Du, Peng Xie
Institution: California State University, East Bay
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W3206576635