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Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring

Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various d...

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Autores principales: Goh, Rui Ying, Lee, Lai Soon, Seow, Hsin-Vonn, Gopal, Kathiresan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597311/
https://www.ncbi.nlm.nih.gov/pubmed/33286758
http://dx.doi.org/10.3390/e22090989
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author Goh, Rui Ying
Lee, Lai Soon
Seow, Hsin-Vonn
Gopal, Kathiresan
author_facet Goh, Rui Ying
Lee, Lai Soon
Seow, Hsin-Vonn
Gopal, Kathiresan
author_sort Goh, Rui Ying
collection PubMed
description Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various data patterns. Both are black-box models which are sensitive to hyperparameter settings. Feature selection can be performed on SVM to enable explanation with the reduced features, whereas feature importance computed by RF can be used for model explanation. The benefits of accuracy and interpretation allow for significant improvement in the area of credit risk and credit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM to perform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tune the hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achieve comparable results as the standard HS with a shorter computational time. MHS consists of four main modifications in the standard HS: (i) Elitism selection during memory consideration instead of random selection, (ii) dynamic exploration and exploitation operators in place of the original static operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional termination criteria to reach faster convergence. Along with parallel computing, MHS effectively reduces the computational time of the proposed hybrid models. The proposed hybrid models are compared with standard statistical models across three different datasets commonly used in credit scoring studies. The computational results show that MHS-RF is most robust in terms of model performance, model explainability and computational time.
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spelling pubmed-75973112020-11-09 Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring Goh, Rui Ying Lee, Lai Soon Seow, Hsin-Vonn Gopal, Kathiresan Entropy (Basel) Article Credit scoring is an important tool used by financial institutions to correctly identify defaulters and non-defaulters. Support Vector Machines (SVM) and Random Forest (RF) are the Artificial Intelligence techniques that have been attracting interest due to their flexibility to account for various data patterns. Both are black-box models which are sensitive to hyperparameter settings. Feature selection can be performed on SVM to enable explanation with the reduced features, whereas feature importance computed by RF can be used for model explanation. The benefits of accuracy and interpretation allow for significant improvement in the area of credit risk and credit scoring. This paper proposes the use of Harmony Search (HS), to form a hybrid HS-SVM to perform feature selection and hyperparameter tuning simultaneously, and a hybrid HS-RF to tune the hyperparameters. A Modified HS (MHS) is also proposed with the main objective to achieve comparable results as the standard HS with a shorter computational time. MHS consists of four main modifications in the standard HS: (i) Elitism selection during memory consideration instead of random selection, (ii) dynamic exploration and exploitation operators in place of the original static operators, (iii) a self-adjusted bandwidth operator, and (iv) inclusion of additional termination criteria to reach faster convergence. Along with parallel computing, MHS effectively reduces the computational time of the proposed hybrid models. The proposed hybrid models are compared with standard statistical models across three different datasets commonly used in credit scoring studies. The computational results show that MHS-RF is most robust in terms of model performance, model explainability and computational time. MDPI 2020-09-04 /pmc/articles/PMC7597311/ /pubmed/33286758 http://dx.doi.org/10.3390/e22090989 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Goh, Rui Ying
Lee, Lai Soon
Seow, Hsin-Vonn
Gopal, Kathiresan
Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring
title Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring
title_full Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring
title_fullStr Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring
title_full_unstemmed Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring
title_short Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring
title_sort hybrid harmony search–artificial intelligence models in credit scoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597311/
https://www.ncbi.nlm.nih.gov/pubmed/33286758
http://dx.doi.org/10.3390/e22090989
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