A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers
Financial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring litera...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
D. G. Pylarinos
2017-10-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/1361 |
| _version_ | 1798001194845601792 |
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| author | A. G. Armaki M. F. Fallah M. Alborzi A. Mohammadzadeh |
| author_facet | A. G. Armaki M. F. Fallah M. Alborzi A. Mohammadzadeh |
| author_sort | A. G. Armaki |
| collection | DOAJ |
| description | Financial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. Moreover, this paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. Hence, it helps us to identify which hybrid model can achieve the best performance for credit scoring purposes. Using four real-life credit datasets, the experimental results show that the model of (KNN-NN-SVMPSO)-(DL)-(DBSCAN) delivers the highest prediction accuracy and the lowest error rates. |
| first_indexed | 2024-04-11T11:32:20Z |
| format | Article |
| id | doaj.art-36d6acb818c34f1cbd62360add9b96de |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-04-11T11:32:20Z |
| publishDate | 2017-10-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-36d6acb818c34f1cbd62360add9b96de2022-12-22T04:26:04ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362017-10-01752073208210.48084/etasr.13611049A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ CustomersA. G. Armaki0M. F. Fallah1M. Alborzi2A. Mohammadzadeh3Department of Management, Islamic Azad University Qazvin, Qazvin, IranTehran Central Branch, Islamic Azad University, Tehran, IranInformation Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Management, Islamic Azad University Qazvin, Qazvin, IranFinancial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. Moreover, this paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. Hence, it helps us to identify which hybrid model can achieve the best performance for credit scoring purposes. Using four real-life credit datasets, the experimental results show that the model of (KNN-NN-SVMPSO)-(DL)-(DBSCAN) delivers the highest prediction accuracy and the lowest error rates.https://etasr.com/index.php/ETASR/article/view/1361credit scoringhybrid machine learning modelsstackingdeep learning |
| spellingShingle | A. G. Armaki M. F. Fallah M. Alborzi A. Mohammadzadeh A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers Engineering, Technology & Applied Science Research credit scoring hybrid machine learning models stacking deep learning |
| title | A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers |
| title_full | A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers |
| title_fullStr | A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers |
| title_full_unstemmed | A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers |
| title_short | A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers |
| title_sort | hybrid meta learner technique for credit scoring of banks customers |
| topic | credit scoring hybrid machine learning models stacking deep learning |
| url | https://etasr.com/index.php/ETASR/article/view/1361 |
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