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MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION

Chi GUOTAI, Abedin Abedin, Fahmida E–MOULA

Journal of Business Economics and Management, Volume: 18, Issue: 2, Pages: 224 - 240

Swansea University Author: Abedin Abedin

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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...

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Published in: Journal of Business Economics and Management
ISSN: 1611-1699 2029-4433
Published: Vilnius Gediminas Technical University
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64252
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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.
Keywords: Credit prediction, neural networks, Multi-Layer Perceptron, hidden neurons, alteration experiments, investigation and optimization
College: Faculty of Humanities and Social Sciences
Issue: 2
Start Page: 224
End Page: 240