Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults
This investigation elucidates the paramount endeavour of predicting loan defaults, which is imperative for the efficacious management of financial risk and the overall stability of financial institutions. Conventional statistical methodologies frequently encounter challenges in effectively capturing...
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| Language: | English |
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Institutul de Studii Financiare
2024-11-01
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| Series: | Revista de Studii Financiare |
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| Online Access: | https://revista.isfin.ro/wp-content/uploads/2024/11/6.-Konstantinos-K.-et..pdf |
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| author | Konstantinos Kofidis Cătălina Lucia Cocianu |
| author_facet | Konstantinos Kofidis Cătălina Lucia Cocianu |
| author_sort | Konstantinos Kofidis |
| collection | DOAJ |
| description | This investigation elucidates the paramount endeavour of predicting loan defaults, which is imperative for the efficacious management of financial risk and the overall stability of financial institutions. Conventional statistical methodologies frequently encounter challenges in effectively capturing the nonlinear and sequential dynamics inherent in financial data, thereby necessitating the examination of more sophisticated machine learning methodologies. This research reports an experimental-based comparative evaluation of three ML and DL models—Long Short-Term Memory (LSTM) networks, Random Forest (RF), and Support Vector Regression (SVR)—to assess their efficacy in forecasting loan defaults. The models are evaluated using metrics such as Mean Squared Error (MSE), F1 score, and Accuracy, and their proficiency in addressing imbalanced datasets and elucidating intricate data relationships is highlighted. The results indicate that while the Random Forest model surpasses its counterparts in terms of accuracy and MSE, the LSTM model exhibits considerable potential in managing imbalanced data, as evidenced by its stable F1 score. Although SVR reveals competitive precision, it exhibits deficiencies in addressing class imbalance. The ANOVA analyses substantiate that the disparities in model performance are statistically significant. The research acknowledges that both the LSTM and SVR models remain in the developmental stages, with ongoing initiatives aimed at refining these models through hyperparameter optimization and advanced architectural frameworks to enhance their predictive efficacy in practical applications. |
| format | Article |
| id | doaj-art-2853703fe5b84e8d980a1d0b3e457ef2 |
| institution | Kabale University |
| issn | 2537-3714 2559-1347 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Institutul de Studii Financiare |
| record_format | Article |
| series | Revista de Studii Financiare |
| spelling | doaj-art-2853703fe5b84e8d980a1d0b3e457ef22025-08-20T03:28:21ZengInstitutul de Studii FinanciareRevista de Studii Financiare2537-37142559-13472024-11-019179110610.55654/JFS.2024.9.17.06Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan DefaultsKonstantinos Kofidis0Cătălina Lucia Cocianu1Bucharest University of Economic Studies, Bucharest, RomaniaBucharest University of Economic Studies, Bucharest, RomaniaThis investigation elucidates the paramount endeavour of predicting loan defaults, which is imperative for the efficacious management of financial risk and the overall stability of financial institutions. Conventional statistical methodologies frequently encounter challenges in effectively capturing the nonlinear and sequential dynamics inherent in financial data, thereby necessitating the examination of more sophisticated machine learning methodologies. This research reports an experimental-based comparative evaluation of three ML and DL models—Long Short-Term Memory (LSTM) networks, Random Forest (RF), and Support Vector Regression (SVR)—to assess their efficacy in forecasting loan defaults. The models are evaluated using metrics such as Mean Squared Error (MSE), F1 score, and Accuracy, and their proficiency in addressing imbalanced datasets and elucidating intricate data relationships is highlighted. The results indicate that while the Random Forest model surpasses its counterparts in terms of accuracy and MSE, the LSTM model exhibits considerable potential in managing imbalanced data, as evidenced by its stable F1 score. Although SVR reveals competitive precision, it exhibits deficiencies in addressing class imbalance. The ANOVA analyses substantiate that the disparities in model performance are statistically significant. The research acknowledges that both the LSTM and SVR models remain in the developmental stages, with ongoing initiatives aimed at refining these models through hyperparameter optimization and advanced architectural frameworks to enhance their predictive efficacy in practical applications.https://revista.isfin.ro/wp-content/uploads/2024/11/6.-Konstantinos-K.-et..pdfloan default predictionfinancial risk managementlong short-term memory (lstm)support vector regression (svr)mean squared errorf1 score |
| spellingShingle | Konstantinos Kofidis Cătălina Lucia Cocianu Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults Revista de Studii Financiare loan default prediction financial risk management long short-term memory (lstm) support vector regression (svr) mean squared error f1 score |
| title | Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults |
| title_full | Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults |
| title_fullStr | Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults |
| title_full_unstemmed | Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults |
| title_short | Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults |
| title_sort | comparative analysis of rf svr with gaussian kernel and lstm for predicting loan defaults |
| topic | loan default prediction financial risk management long short-term memory (lstm) support vector regression (svr) mean squared error f1 score |
| url | https://revista.isfin.ro/wp-content/uploads/2024/11/6.-Konstantinos-K.-et..pdf |
| work_keys_str_mv | AT konstantinoskofidis comparativeanalysisofrfsvrwithgaussiankernelandlstmforpredictingloandefaults AT catalinaluciacocianu comparativeanalysisofrfsvrwithgaussiankernelandlstmforpredictingloandefaults |