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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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DOI (Published version): 10.3846/16111699.2017.1280844

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
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URI: https://cronfa.swan.ac.uk/Record/cronfa64252
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first_indexed 2023-09-19T10:49:59Z
last_indexed 2023-09-19T10:49:59Z
id cronfa64252
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spelling v2 64252 2023-08-31 MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF 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. Journal Article Journal of Business Economics and Management 18 2 224 240 Vilnius Gediminas Technical University 1611-1699 2029-4433 Credit prediction, neural networks, Multi-Layer Perceptron, hidden neurons, alteration experiments, investigation and optimization 0 0 0 0001-01-01 10.3846/16111699.2017.1280844 http://dx.doi.org/10.3846/16111699.2017.1280844 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-20T10:34:57.3450303 2023-08-31T17:53:40.2041675 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Chi GUOTAI 1 Abedin Abedin 2 Fahmida E–MOULA 3
title MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION
spellingShingle MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION
Abedin Abedin
title_short MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION
title_full MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION
title_fullStr MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION
title_full_unstemmed MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION
title_sort MODELING CREDIT APPROVAL DATA WITH NEURAL NETWORKS: AN EXPERIMENTAL INVESTIGATION AND OPTIMIZATION
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Chi GUOTAI
Abedin Abedin
Fahmida E–MOULA
format Journal article
container_title Journal of Business Economics and Management
container_volume 18
container_issue 2
container_start_page 224
institution Swansea University
issn 1611-1699
2029-4433
doi_str_mv 10.3846/16111699.2017.1280844
publisher Vilnius Gediminas Technical University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.3846/16111699.2017.1280844
document_store_str 0
active_str 0
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.
published_date 0001-01-01T10:34:54Z
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score 11.012678

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