Record Details

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

The Journal of Business Economics and Management

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Field Value
 
Title Modeling credit approval data with neural networks: an experimental investigation and optimization
 
Creator Guotai, Chi
Abedin, Mohammad Zoynul
E–moula, Fahmida
 
Subject credit prediction
neural networks
Multi-Layer Perceptron
hidden neurons
alteration experiments
investigation and optimization
 
Description 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.
 
Publisher VGTU Press Technika
 
Date 2017-04-21
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
application/pdf
 
Identifier https://journals.vgtu.lt/index.php/JBEM/article/view/1181
10.3846/16111699.2017.1280844
 
Source Journal of Business Economics and Management; Vol 18 No 2 (2017); 224-240
2029-4433
1611-1699
 
Language eng
 
Relation https://journals.vgtu.lt/index.php/JBEM/article/view/1181/923
https://journals.vgtu.lt/index.php/JBEM/article/view/1181/931