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Modeling credit approval data with neural networks: an experimental investigation and optimization

    Chi Guotai Affiliation
    ; Mohammad Zoynul Abedin Affiliation
    ; Fahmida E–moula Affiliation

Abstract

This study proposes an investigation and optimization of Multi-Layer Perceptron (MLP) based artificial neural networks (ANN) credit prediction model, combine with the effect of different ratios of training to testing instances over five real-world credit databases. As an outcome from the alteration procedure, three different types of hidden units [K = 9 (ANN–1), K = 10 (ANN–2), K = 23 (ANN–3)] are chosen through the pilot experiments and execute, therefore, 45 (5×3×3) unique neural models. Experimental results indicate that “the neural architecture with ten hidden units” is proposed as an optimal approach to classifying the credit information. With these contributions, therefore, we complement previous evidence and modernize the methods of credit prediction modeling. This study, however, has realistic implications for bank managers and other stakeholders to delineate the risk profile of the credit customers.

Keyword : credit prediction, neural networks, Multi-Layer Perceptron, hidden neurons, alteration experiments, investigation and optimization

How to Cite
Guotai, C., Abedin, M., & E–moula, F. (2017). Modeling credit approval data with neural networks: an experimental investigation and optimization. Journal of Business Economics and Management, 18(2), 224-240. https://doi.org/10.3846/16111699.2017.1280844
Published in Issue
Apr 21, 2017
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