Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques
A loan is vital for individuals and organizations to meet their goals. However, financial institutions face challenges like managing losses and missed opportunities in loan decisions. A key issue is the imbalanced datasets in credit risk assessment, hindering accurate predictions of defaulters. Prev...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-12-01
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| Series: | Cybernetics and Information Technologies |
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| Online Access: | https://doi.org/10.2478/cait-2024-0034 |
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| author | Soni Umangbhai Jethava Gordhan Ganatra Amit |
| author_facet | Soni Umangbhai Jethava Gordhan Ganatra Amit |
| author_sort | Soni Umangbhai |
| collection | DOAJ |
| description | A loan is vital for individuals and organizations to meet their goals. However, financial institutions face challenges like managing losses and missed opportunities in loan decisions. A key issue is the imbalanced datasets in credit risk assessment, hindering accurate predictions of defaulters. Previous research has utilized machine learning techniques, including single or multiple classifier systems, ensemble methods, and class-balancing approaches. This review summarizes various factors and machine learning methods for assessing credit risk, presented in a tabular format to provide valuable insights for researchers. It covers data complexity, minority class distribution, sampling techniques, feature selection, and meta-learning parameters. The goal is to help develop novel algorithms that outperform existing methods. Even a slight improvement in defaulter prediction rates could significantly influence society by saving millions for lenders. |
| format | Article |
| id | doaj-art-7bc79f7db1bd448d9a2e956cd7e19fa6 |
| institution | DOAJ |
| issn | 1314-4081 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj-art-7bc79f7db1bd448d9a2e956cd7e19fa62025-08-20T02:44:02ZengSciendoCybernetics and Information Technologies1314-40812024-12-01244224410.2478/cait-2024-0034Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning TechniquesSoni Umangbhai0Jethava Gordhan1Ganatra Amit2Department of Computer Science and Engineering, Parul Institute of Engineering & Technology, FET, Parul University, Vadodara, 391760, Gujarat, IndiaDepartment of Information Technology, Parul Institute of Engineering & Technology, FET, Parul University, Vadodara, 391760, Gujarat, IndiaParul University, Vadodara, 391760, Gujarat, IndiaA loan is vital for individuals and organizations to meet their goals. However, financial institutions face challenges like managing losses and missed opportunities in loan decisions. A key issue is the imbalanced datasets in credit risk assessment, hindering accurate predictions of defaulters. Previous research has utilized machine learning techniques, including single or multiple classifier systems, ensemble methods, and class-balancing approaches. This review summarizes various factors and machine learning methods for assessing credit risk, presented in a tabular format to provide valuable insights for researchers. It covers data complexity, minority class distribution, sampling techniques, feature selection, and meta-learning parameters. The goal is to help develop novel algorithms that outperform existing methods. Even a slight improvement in defaulter prediction rates could significantly influence society by saving millions for lenders.https://doi.org/10.2478/cait-2024-0034credit risk assessmentsingle classifier systemmultiple classifier systemdynamic selectionclass-imbalance. |
| spellingShingle | Soni Umangbhai Jethava Gordhan Ganatra Amit Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques Cybernetics and Information Technologies credit risk assessment single classifier system multiple classifier system dynamic selection class-imbalance. |
| title | Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques |
| title_full | Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques |
| title_fullStr | Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques |
| title_full_unstemmed | Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques |
| title_short | Latest Advancements in Credit Risk Assessment with Machine Learning and Deep Learning Techniques |
| title_sort | latest advancements in credit risk assessment with machine learning and deep learning techniques |
| topic | credit risk assessment single classifier system multiple classifier system dynamic selection class-imbalance. |
| url | https://doi.org/10.2478/cait-2024-0034 |
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