Bank Term Deposit Subscription Recommendation Using Cross-validated Neural Network

Term deposits can help to accelerate the financial field by increasing profit for both the bank and the customer. The bank’s promotion efforts, as well as the customer’s background information, often affect term deposit subscription. If the bank can identify clients’ subscription tendencies early on, it can change its fundamental strategy to attract additional customers. The current work has focused on identifying term likelihood prediction from the customer’s perspective in this setting. Machine learning-based algorithms have been used to predict term deposit investment opportunities in advance for this study. Neural network, a common machine learning-based method, is proposed as the predictive model in this study, along with stratified 10-fold cross-validation methodology. Other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP) are also implemented and compared to assess the efficiency of this model. With an accuracy of 88.32 percent and an MSE of 0.1168, this comparison analysis found that the suggested model outperforms existing baseline models in terms of prediction results.

Author(S) Details

Shawni Dutta
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.

Payal Bose
Lincoln University College, Kota Bharu, Kelantan, Malaysia.

Samir Kumar Bandyopadhyay
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.

View Book:- https://stm.bookpi.org/NIEBM-V2/article/view/4737

Related Posts

Leave a Reply

Your email address will not be published. Required fields are marked *