Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk
Rating consumer credit risk involves assessing credit application risks. Thus, every business must appropriately identify debtors and non-debtors. This study uses machine learning approaches to simulate consumer credit risk and compares the results to the logistic model, determining if machine learn...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/25733/ http://journalarticle.ukm.my/25733/1/13.pdf |
| _version_ | 1848816436077658112 |
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| author | Elaf Adel Abbas, Nisreen Abbas Hussein, |
| author_facet | Elaf Adel Abbas, Nisreen Abbas Hussein, |
| author_sort | Elaf Adel Abbas, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Rating consumer credit risk involves assessing credit application risks. Thus, every business must appropriately identify debtors and non-debtors. This study uses machine learning approaches to simulate consumer credit risk and compares the results to the logistic model, determining if machine learning improves client default ratings. The study examines how customer attributes affect virtual experiences. Despite advances in machine learning models for credit assessment, unbalanced datasets and some algorithms’ failure to explain forecasts remain major issues. This study used 2005 Taiwanese credit card consumers’ education, age, marital status, payment history, and sex. The default experience is modeled using Logistic Regression, K neighbors, Support Vector Machine, Decision Tree, Random Forest, Ada Boost Classifier, and Gradient Boosting. The models’ Accuracy, precision, recall, receiver operating characteristic (ROC) curve, and precision-recall curve were evaluated. Random Forest’s 97% ROC metric rating outperformed all other accuracy metrics. The logistic model underperformed, while machine learning improved the default categorization. |
| first_indexed | 2025-11-15T01:05:50Z |
| format | Article |
| id | oai:generic.eprints.org:25733 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:05:50Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:257332025-08-12T01:45:56Z http://journalarticle.ukm.my/25733/ Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk Elaf Adel Abbas, Nisreen Abbas Hussein, Rating consumer credit risk involves assessing credit application risks. Thus, every business must appropriately identify debtors and non-debtors. This study uses machine learning approaches to simulate consumer credit risk and compares the results to the logistic model, determining if machine learning improves client default ratings. The study examines how customer attributes affect virtual experiences. Despite advances in machine learning models for credit assessment, unbalanced datasets and some algorithms’ failure to explain forecasts remain major issues. This study used 2005 Taiwanese credit card consumers’ education, age, marital status, payment history, and sex. The default experience is modeled using Logistic Regression, K neighbors, Support Vector Machine, Decision Tree, Random Forest, Ada Boost Classifier, and Gradient Boosting. The models’ Accuracy, precision, recall, receiver operating characteristic (ROC) curve, and precision-recall curve were evaluated. Random Forest’s 97% ROC metric rating outperformed all other accuracy metrics. The logistic model underperformed, while machine learning improved the default categorization. Penerbit Universiti Kebangsaan Malaysia 2024-09 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/25733/1/13.pdf Elaf Adel Abbas, and Nisreen Abbas Hussein, (2024) Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk. Jurnal Kejuruteraan, 36 (5). pp. 1935-1944. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3605-2024/ |
| spellingShingle | Elaf Adel Abbas, Nisreen Abbas Hussein, Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk |
| title | Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk |
| title_full | Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk |
| title_fullStr | Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk |
| title_full_unstemmed | Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk |
| title_short | Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk |
| title_sort | algorithm comparison for data mining classification: assessing bank customer credit scoring default risk |
| url | http://journalarticle.ukm.my/25733/ http://journalarticle.ukm.my/25733/ http://journalarticle.ukm.my/25733/1/13.pdf |