Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction
Credit default prediction is an important problem in financial risk management. It aims to determine the possibility of borrowers failing on their loan commitments. However, dataset to guide Machine Learning modeling procedure for data driven support suffers from class imbalance. Class imbalance in...
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Accademia Piceno Aprutina dei Velati
2024-12-01
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| Series: | Ratio Mathematica |
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| Online Access: | http://eiris.it/ojs/index.php/ratiomathematica/article/view/1601 |
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| author | Emmanuel de-Graft Johnson Owusu-Ansah Richard Doamekpor Richard Kodzo Avuglah Yaa Kyere Adwubi |
| author_facet | Emmanuel de-Graft Johnson Owusu-Ansah Richard Doamekpor Richard Kodzo Avuglah Yaa Kyere Adwubi |
| author_sort | Emmanuel de-Graft Johnson Owusu-Ansah |
| collection | DOAJ |
| description | Credit default prediction is an important problem in financial risk management. It aims to determine the possibility of borrowers failing on their loan commitments. However, dataset to guide Machine Learning modeling procedure for data driven support suffers from class imbalance. Class imbalance in Machine Learning is an unbalanced distribution of classes within a dataset. This problem often arises in classification jobs if the distribution of classes or labels in a dataset is not uniform. To overcome this issue, just resample by adding or removing entries from the minority or majority classes.
The present study looks on the efficacy of classification algorithms employing various data balancing approaches. The dataset was collected from a well-known commercial bank in Ghana. To resolve the imbalance, three data balancing approaches were used: under-sampling, oversampling, and the synthetic minority oversampling technique (SMOTE). Findings, with the exception of the SMOTE dataset, XGBoost consistently beat the other classifiers across the other datasets in terms of AUC. Random forest, decision tree, and logistic regression all performed well and might be utilized as alternatives to XGBoost classifiers for developing credit scoring models. The findings demonstrate that classifiers trained on balanced datasets have higher sensitivity scores than those trained on the original skewed dataset, while maintaining their capacity to differentiate between defaulters and non-defaulters. This demonstrates the value of data balancing strategies in increasing models' ability to anticipate minority class occurrences, Hence, the major discovery is that oversampling outperforms under-sampling across classifiers and evaluation measures is affirmed. |
| format | Article |
| id | doaj-art-d6235b7b2ad543cb9c9ddf2b882cb5db |
| institution | Kabale University |
| issn | 1592-7415 2282-8214 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Accademia Piceno Aprutina dei Velati |
| record_format | Article |
| series | Ratio Mathematica |
| spelling | doaj-art-d6235b7b2ad543cb9c9ddf2b882cb5db2025-02-01T06:51:01ZengAccademia Piceno Aprutina dei VelatiRatio Mathematica1592-74152282-82142024-12-0153010.23755/rm.v53i0.1601949Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default PredictionEmmanuel de-Graft Johnson Owusu-Ansah0Richard Doamekpor1Richard Kodzo Avuglah2Yaa Kyere Adwubi3Department of Statistics and Actuarial Science Kwame Nkrumah University of Science and Technology, Kumasi .Department of Statistics and Actuarial Science Kwame Nkrumah University of Science and Technology, KumasiDepartment of Statistics and Actuarial Science Kwame Nkrumah University of Science and Technology, KumasiDepartment of Statistics and Actuarial Science Kwame Nkrumah University of Science and Technology, KumasiCredit default prediction is an important problem in financial risk management. It aims to determine the possibility of borrowers failing on their loan commitments. However, dataset to guide Machine Learning modeling procedure for data driven support suffers from class imbalance. Class imbalance in Machine Learning is an unbalanced distribution of classes within a dataset. This problem often arises in classification jobs if the distribution of classes or labels in a dataset is not uniform. To overcome this issue, just resample by adding or removing entries from the minority or majority classes. The present study looks on the efficacy of classification algorithms employing various data balancing approaches. The dataset was collected from a well-known commercial bank in Ghana. To resolve the imbalance, three data balancing approaches were used: under-sampling, oversampling, and the synthetic minority oversampling technique (SMOTE). Findings, with the exception of the SMOTE dataset, XGBoost consistently beat the other classifiers across the other datasets in terms of AUC. Random forest, decision tree, and logistic regression all performed well and might be utilized as alternatives to XGBoost classifiers for developing credit scoring models. The findings demonstrate that classifiers trained on balanced datasets have higher sensitivity scores than those trained on the original skewed dataset, while maintaining their capacity to differentiate between defaulters and non-defaulters. This demonstrates the value of data balancing strategies in increasing models' ability to anticipate minority class occurrences, Hence, the major discovery is that oversampling outperforms under-sampling across classifiers and evaluation measures is affirmed.http://eiris.it/ojs/index.php/ratiomathematica/article/view/1601credit scoring, smote, oversampling, undersampling, class imbalance, machine learning algorithms |
| spellingShingle | Emmanuel de-Graft Johnson Owusu-Ansah Richard Doamekpor Richard Kodzo Avuglah Yaa Kyere Adwubi Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction Ratio Mathematica credit scoring, smote, oversampling, undersampling, class imbalance, machine learning algorithms |
| title | Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction |
| title_full | Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction |
| title_fullStr | Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction |
| title_full_unstemmed | Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction |
| title_short | Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction |
| title_sort | machine learning algorithms analysis of synthetic minority oversampling technique smote application to credit default prediction |
| topic | credit scoring, smote, oversampling, undersampling, class imbalance, machine learning algorithms |
| url | http://eiris.it/ojs/index.php/ratiomathematica/article/view/1601 |
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