Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing
The prediction of loan defaults is crucial for banks and financial institutions due to its impact on earnings, and it also plays a significant role in shaping credit scores. This task is a challenging one, and as the demand for loans increases, so does the number of applications. Traditional methods...
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IEEE
2024-01-01
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| author | Eslam Hussein Sayed Amerah Alabrah Kamel Hussein Rahouma Muhammad Zohaib Rasha M. Badry |
| author_facet | Eslam Hussein Sayed Amerah Alabrah Kamel Hussein Rahouma Muhammad Zohaib Rasha M. Badry |
| author_sort | Eslam Hussein Sayed |
| collection | DOAJ |
| description | The prediction of loan defaults is crucial for banks and financial institutions due to its impact on earnings, and it also plays a significant role in shaping credit scores. This task is a challenging one, and as the demand for loans increases, so does the number of applications. Traditional methods of checking eligibility are time-consuming and laborious, and they may not always accurately identify suitable loan recipients. As a result, some applicants may default on their loans, causing financial losses for banks. Artificial Intelligence, using Machine Learning and Deep Learning techniques, can provide a more efficient solution. These techniques can use various classification algorithms to predict which applicants will likely be eligible for loans. This study uses five Machine Learning classification algorithms (Gaussian Naive Bayes, AdaBoost, Gradient Boosting, K Neighbors Classifier, Decision Trees, Random Forest, and Logistic Regression) and eight Deep Learning algorithms (MLP, CNN, LSTM, Transformer, GRU, Autoencoder, ResNet, and DenseNet). The use of Ensemble Methods and SMOTE with SMOTE-TOMEK Techniques also has a positive impact on the results. Four metrics are used to evaluate the effectiveness of these algorithms: accuracy, precision, recall, and F1-measure. The study found that DenseNet and ResNet were the most accurate predictive models. These findings highlight the potential of predictive modeling in identifying credit disapproval among vulnerable consumers in a sea of loan applications. |
| format | Article |
| id | doaj-art-94ab608b561a4bbaa9fa66e7cc778342 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-94ab608b561a4bbaa9fa66e7cc7783422025-08-20T02:00:10ZengIEEEIEEE Access2169-35362024-01-011219399719401910.1109/ACCESS.2024.350977410772107Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data BalancingEslam Hussein Sayed0https://orcid.org/0009-0007-0909-1702Amerah Alabrah1https://orcid.org/0000-0001-9750-3883Kamel Hussein Rahouma2Muhammad Zohaib3Rasha M. Badry4https://orcid.org/0000-0002-8094-5930Information System Department, Faculty of Computers and Information, Fayoum University, Faiyum, EgyptDepartment of Information Systems, College of Computer and Information Science, King Saud University, Riyadh, Saudi ArabiaElectrical Engineering Department, Faculty of Engineering, Minia University, Minya, EgyptSoftware Engineering Department, Lappeenranta-Lahti University of Technology, Lappeenranta, FinlandInformation System Department, Faculty of Computers and Information, Fayoum University, Faiyum, EgyptThe prediction of loan defaults is crucial for banks and financial institutions due to its impact on earnings, and it also plays a significant role in shaping credit scores. This task is a challenging one, and as the demand for loans increases, so does the number of applications. Traditional methods of checking eligibility are time-consuming and laborious, and they may not always accurately identify suitable loan recipients. As a result, some applicants may default on their loans, causing financial losses for banks. Artificial Intelligence, using Machine Learning and Deep Learning techniques, can provide a more efficient solution. These techniques can use various classification algorithms to predict which applicants will likely be eligible for loans. This study uses five Machine Learning classification algorithms (Gaussian Naive Bayes, AdaBoost, Gradient Boosting, K Neighbors Classifier, Decision Trees, Random Forest, and Logistic Regression) and eight Deep Learning algorithms (MLP, CNN, LSTM, Transformer, GRU, Autoencoder, ResNet, and DenseNet). The use of Ensemble Methods and SMOTE with SMOTE-TOMEK Techniques also has a positive impact on the results. Four metrics are used to evaluate the effectiveness of these algorithms: accuracy, precision, recall, and F1-measure. The study found that DenseNet and ResNet were the most accurate predictive models. These findings highlight the potential of predictive modeling in identifying credit disapproval among vulnerable consumers in a sea of loan applications.https://ieeexplore.ieee.org/document/10772107/Customer loan predictionartificial intelligencedata preprocessingmodel optimizationmachine learningdeep learning |
| spellingShingle | Eslam Hussein Sayed Amerah Alabrah Kamel Hussein Rahouma Muhammad Zohaib Rasha M. Badry Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing IEEE Access Customer loan prediction artificial intelligence data preprocessing model optimization machine learning deep learning |
| title | Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing |
| title_full | Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing |
| title_fullStr | Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing |
| title_full_unstemmed | Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing |
| title_short | Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing |
| title_sort | machine learning and deep learning for loan prediction in banking exploring ensemble methods and data balancing |
| topic | Customer loan prediction artificial intelligence data preprocessing model optimization machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/10772107/ |
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