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Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches

Guotai Chi, Mohammad Shamsu Uddin, Mohammad Abedin Orcid Logo, Kunpeng Yuan

International Journal on Artificial Intelligence Tools, Volume: 28, Issue: 05, Start page: 1950017

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1142/s0218213019500179

Abstract

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI...

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Published in: International Journal on Artificial Intelligence Tools
ISSN: 0218-2130 1793-6349
Published: World Scientific Pub Co Pte Lt 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa64277
first_indexed 2023-09-18T12:58:33Z
last_indexed 2024-11-25T14:13:46Z
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spelling 2023-09-19T16:05:25.2883243 v2 64277 2023-08-31 Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets. Journal Article International Journal on Artificial Intelligence Tools 28 05 1950017 World Scientific Pub Co Pte Lt 0218-2130 1793-6349 Credit risk prediction, hybrid model, traditional methods, artificial intelligence (AI) 1 8 2019 2019-08-01 10.1142/s0218213019500179 http://dx.doi.org/10.1142/s0218213019500179 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2023-09-19T16:05:25.2883243 2023-08-31T19:10:05.0217350 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Guotai Chi 1 Mohammad Shamsu Uddin 2 Mohammad Abedin 0000-0002-4688-0619 3 Kunpeng Yuan 4
title Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches
spellingShingle Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches
Mohammad Abedin
title_short Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches
title_full Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches
title_fullStr Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches
title_full_unstemmed Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches
title_sort Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Guotai Chi
Mohammad Shamsu Uddin
Mohammad Abedin
Kunpeng Yuan
format Journal article
container_title International Journal on Artificial Intelligence Tools
container_volume 28
container_issue 05
container_start_page 1950017
publishDate 2019
institution Swansea University
issn 0218-2130
1793-6349
doi_str_mv 10.1142/s0218213019500179
publisher World Scientific Pub Co Pte Lt
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/10.1142/s0218213019500179
document_store_str 0
active_str 0
description Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.
published_date 2019-08-01T08:24:03Z
_version_ 1821393125464080384
score 11.047501

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