Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam
This study utilizes machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest, in the early warning system for debt group migration in a Vietnamese commercial bank. In predicting customers’ overdue debt migration (B Score), the RF model achieves...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-09-01
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| Series: | Foundations of Management |
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| Online Access: | https://doi.org/10.2478/fman-2024-0012 |
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| author | Nguyen Quoc Hung Trinh Hoang Viet Phuong Truong Viet Ly Truong Thi Minh |
| author_facet | Nguyen Quoc Hung Trinh Hoang Viet Phuong Truong Viet Ly Truong Thi Minh |
| author_sort | Nguyen Quoc Hung |
| collection | DOAJ |
| description | This study utilizes machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest, in the early warning system for debt group migration in a Vietnamese commercial bank. In predicting customers’ overdue debt migration (B Score), the RF model achieves the highest accuracy of 81.84%. However, if the priority is to reduce Type I errors, SVM performs better with a recall of 91.48%, although the accuracy drops to 46.62%. When predicting customers’ debt group improvement (C Score), SVM proves to be the optimal model in terms of both accuracy and criteria based on Type II errors, with an accuracy of 71.6% and precision of 62.3%. When applied to new datasets, the evaluation criteria decrease, but SVM remains the most optimal model for both B Score and C Score. Additionally, the research results demonstrate that tuning the model parameters leads to a significant improvement in accuracy compared to the default parameters. |
| format | Article |
| id | doaj-art-770b74c70e3d4d55a990d55a9b6dd149 |
| institution | Kabale University |
| issn | 2300-5661 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Foundations of Management |
| spelling | doaj-art-770b74c70e3d4d55a990d55a9b6dd1492025-02-02T15:48:06ZengSciendoFoundations of Management2300-56612024-09-0116119521610.2478/fman-2024-0012Early Warning System for Debt Group Migration: The Case of One Commercial Bank in VietnamNguyen Quoc Hung0Trinh Hoang Viet1Phuong Truong Viet2Ly Truong Thi Minh3University of Economics Ho Chi Minh City, Ho Chi Minh City, VietnamUniversity of Economics Ho Chi Minh City, Ho Chi Minh City, VietnamUniversity of Economics Ho Chi Minh City, Ho Chi Minh City, VietnamUniversity of Economics Ho Chi Minh City, Ho Chi Minh City, VietnamThis study utilizes machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest, in the early warning system for debt group migration in a Vietnamese commercial bank. In predicting customers’ overdue debt migration (B Score), the RF model achieves the highest accuracy of 81.84%. However, if the priority is to reduce Type I errors, SVM performs better with a recall of 91.48%, although the accuracy drops to 46.62%. When predicting customers’ debt group improvement (C Score), SVM proves to be the optimal model in terms of both accuracy and criteria based on Type II errors, with an accuracy of 71.6% and precision of 62.3%. When applied to new datasets, the evaluation criteria decrease, but SVM remains the most optimal model for both B Score and C Score. Additionally, the research results demonstrate that tuning the model parameters leads to a significant improvement in accuracy compared to the default parameters.https://doi.org/10.2478/fman-2024-0012machine learning modelsdebt group migrationb scorec scoremodel parameters tuninge50e51g33g21g24 |
| spellingShingle | Nguyen Quoc Hung Trinh Hoang Viet Phuong Truong Viet Ly Truong Thi Minh Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam Foundations of Management machine learning models debt group migration b score c score model parameters tuning e50 e51 g33 g21 g24 |
| title | Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam |
| title_full | Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam |
| title_fullStr | Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam |
| title_full_unstemmed | Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam |
| title_short | Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam |
| title_sort | early warning system for debt group migration the case of one commercial bank in vietnam |
| topic | machine learning models debt group migration b score c score model parameters tuning e50 e51 g33 g21 g24 |
| url | https://doi.org/10.2478/fman-2024-0012 |
| work_keys_str_mv | AT nguyenquochung earlywarningsystemfordebtgroupmigrationthecaseofonecommercialbankinvietnam AT trinhhoangviet earlywarningsystemfordebtgroupmigrationthecaseofonecommercialbankinvietnam AT phuongtruongviet earlywarningsystemfordebtgroupmigrationthecaseofonecommercialbankinvietnam AT lytruongthiminh earlywarningsystemfordebtgroupmigrationthecaseofonecommercialbankinvietnam |