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

Abstract

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

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Al-Kindi Center for Research and Development (KCRD) (E-Journals)

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Last time updated on 15/05/2024

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