Article: Machine learning approach to predict purchase decision of bank products and services Journal: International Journal of Advanced Intelligence Paradigms (IJAIP) 2025 Vol.30 No.2 pp.118 - 130 Abstract: We propose a machine learning approach to predict purchase decision of bank products and services. The data were collected from May 2008 to May 2014 of a Portuguese bank. This investigation will help to predict the business of the bank and financial inflation and recent trends in bank product and services. The investigation is focused on the classification and prediction of bank telemarketing calls for term deposit product. We have analysed a large dataset of 41,188 observations related with bank client, product, services and socioeconomic attributes. Initially, the dataset was having 150 features and we have selected 21 most relevant features using standard adaptive forward selection and intelligence quotient. We have also compared four machine learning approaches: conditional inference trees (Ctree), recursive partitioning (Rpart), support vector machines (SVM) and random forest. The paper contains an impact analysis of changing training dataset and training time of a model. Observatory study shows the integration of both parameters: accuracy and model learning time to form a generalised and optimised solution for predicting bank business. Inderscience Publishers - linking academia, business and industry through research

Title: Machine learning approach to predict purchase decision of bank products and services

Authors: Saumya Chaturvedi; Vimal Mishra; Nitin Mishra

Addresses: Dr. A.P.J. Abdul Kalam Technical University, Sec-11, Jankipuram Vistar, Lucknow, Uttar Pradesh, India ' Institute of Engineering and Rural Technology, 26, Chatham Line, Prayagraj (Allahabad), UP – 211002, India ' Rajeev Gandhi Memorial College of Engineering and Technology, Nerawada 'X' Roads, Kurnool District, Nandyal, Andhra Pradesh 518501, India

Abstract: We propose a machine learning approach to predict purchase decision of bank products and services. The data were collected from May 2008 to May 2014 of a Portuguese bank. This investigation will help to predict the business of the bank and financial inflation and recent trends in bank product and services. The investigation is focused on the classification and prediction of bank telemarketing calls for term deposit product. We have analysed a large dataset of 41,188 observations related with bank client, product, services and socioeconomic attributes. Initially, the dataset was having 150 features and we have selected 21 most relevant features using standard adaptive forward selection and intelligence quotient. We have also compared four machine learning approaches: conditional inference trees (Ctree), recursive partitioning (Rpart), support vector machines (SVM) and random forest. The paper contains an impact analysis of changing training dataset and training time of a model. Observatory study shows the integration of both parameters: accuracy and model learning time to form a generalised and optimised solution for predicting bank business.

Keywords: machine learning; business intelligence; data mining; decision support systems.

DOI: 10.1504/IJAIP.2025.146970

International Journal of Advanced Intelligence Paradigms, 2025 Vol.30 No.2, pp.118 - 130

Received: 25 Oct 2018
Accepted: 22 Dec 2018
Published online: 10 Jul 2025 *

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Title: Machine learning approach to predict purchase decision of bank products and services

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