Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior

This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep learning methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest and SVM. Results show deep le...

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Bibliographic Details
Published in:Journal of Business and Management Studies 2024-05, Vol.6 (3), p.21-27
Main Authors: Md Nasir Uddin Rana, Sarder Abdulla Al Shiam, Sarmin Akter Shochona, Md Rasibul Islam, Md Asrafuzzaman, Proshanta Kumar Bhowmik, Refat Naznin, Sandip Kumar Ghosh, Md Ariful Islam Sarkar, Md Asaduzzaman
Format: Article
Language:English
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Summary:This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep learning methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest and SVM. Results show deep learning models, particularly ResNet50, outperform traditional ones, with an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure and expertise is crucial for successful deep learning integration, offering a competitive edge in banking decision-making.
ISSN:2709-0876
2709-0876
DOI:10.32996/jbms.2024.6.3.3