Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning Algorithms
In the literature, new machine learning algorithms are dynamically produced in the field of artificial intelligence engineering and the algorithms are constantly updated with new parameter estimations. The performance of existing algorithms in various business areas is still an important topic of di...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/46/e3sconf_icmsem2023_05013.pdf |
| _version_ | 1797756132205264896 |
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| author | Çetin Ali I˙hsan Ahmed Syed Ejaz |
| author_facet | Çetin Ali I˙hsan Ahmed Syed Ejaz |
| author_sort | Çetin Ali I˙hsan |
| collection | DOAJ |
| description | In the literature, new machine learning algorithms are dynamically produced in the field of artificial intelligence engineering and the algorithms are constantly updated with new parameter estimations. The performance of existing algorithms in various business areas is still an important topic of discussion. Also, machine learning algorithms are frequently used in long-term credit ratings, which is an crucially important sub-branch of finance. This study was conducted to determine which popular machine learning model performs better in credit scoring. Artificial Neural Network, Random Forest, Support Vector Machine and K Nearest Neighbor were used to determine the algorithm that is suitable for the structure, attribute content and distribution of the data, and the operating logic of the models. In the study, the long-term credit rating is the target variable and the remaining variables are the features, the prediction performances of these 4 algorithm, which are frequently used in previous studies such as credit rating, credit risk, fraud analysis were compared. After data preprocessing, a classification study was carried out using the features included in the model. The metrics used in the comparison are MSE, RMSE, MAE and accuracy. According to the metrics, RF algorithm showed the best performance in the credit scoring. |
| first_indexed | 2024-03-12T17:57:07Z |
| format | Article |
| id | doaj.art-0b390819f98647bdab257504f8fc91c1 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T17:57:07Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-0b390819f98647bdab257504f8fc91c12023-08-02T13:21:23ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014090501310.1051/e3sconf/202340905013e3sconf_icmsem2023_05013Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning AlgorithmsÇetin Ali I˙hsan0Ahmed Syed Ejaz1Statistics Dep., Faculty of Science, Yildiz Technical UniversityFaculty of Math & Science, Brock UniversityIn the literature, new machine learning algorithms are dynamically produced in the field of artificial intelligence engineering and the algorithms are constantly updated with new parameter estimations. The performance of existing algorithms in various business areas is still an important topic of discussion. Also, machine learning algorithms are frequently used in long-term credit ratings, which is an crucially important sub-branch of finance. This study was conducted to determine which popular machine learning model performs better in credit scoring. Artificial Neural Network, Random Forest, Support Vector Machine and K Nearest Neighbor were used to determine the algorithm that is suitable for the structure, attribute content and distribution of the data, and the operating logic of the models. In the study, the long-term credit rating is the target variable and the remaining variables are the features, the prediction performances of these 4 algorithm, which are frequently used in previous studies such as credit rating, credit risk, fraud analysis were compared. After data preprocessing, a classification study was carried out using the features included in the model. The metrics used in the comparison are MSE, RMSE, MAE and accuracy. According to the metrics, RF algorithm showed the best performance in the credit scoring.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/46/e3sconf_icmsem2023_05013.pdfmachine learningclassificationfinancedata mining |
| spellingShingle | Çetin Ali I˙hsan Ahmed Syed Ejaz Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning Algorithms E3S Web of Conferences machine learning classification finance data mining |
| title | Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning Algorithms |
| title_full | Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning Algorithms |
| title_fullStr | Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning Algorithms |
| title_full_unstemmed | Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning Algorithms |
| title_short | Determinants of Credit Ratings and Comparison of the Rating Prediction Performances of Machine Learning Algorithms |
| title_sort | determinants of credit ratings and comparison of the rating prediction performances of machine learning algorithms |
| topic | machine learning classification finance data mining |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/46/e3sconf_icmsem2023_05013.pdf |
| work_keys_str_mv | AT cetinaliihsan determinantsofcreditratingsandcomparisonoftheratingpredictionperformancesofmachinelearningalgorithms AT ahmedsyedejaz determinantsofcreditratingsandcomparisonoftheratingpredictionperformancesofmachinelearningalgorithms |