Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits
This systematic literature review aims to identify key variables and measurement methods for determining maximum credit loan limits, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The complexity of setting an optimal credit limit to manage credit ri...
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| Format: | Article |
| Language: | English |
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P3M Politeknik Negeri Banjarmasin
2024-12-01
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| Series: | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
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| Online Access: | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1156 |
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| _version_ | 1850251819420221440 |
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| author | Algies Rifkha Fadillah Mohamad Nurkamal Fauzan |
| author_facet | Algies Rifkha Fadillah Mohamad Nurkamal Fauzan |
| author_sort | Algies Rifkha Fadillah |
| collection | DOAJ |
| description | This systematic literature review aims to identify key variables and measurement methods for determining maximum credit loan limits, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The complexity of setting an optimal credit limit to manage credit risk effectively presents a significant challenge. Establishing an efficient maximum loan limit is essential to mitigate credit risk, as an overly high limit increases default potential, while an excessively low limit restricts the financial institution's growth. This study identifies key variables and measurement methods, including Machine Learning techniques, Neural Networks, and traditional statistical approaches. Machine Learning models, such as Random Forest and Gradient Boosting, often surpass traditional methods in handling large, unstructured datasets due to their capacity for modeling complex, non-linear relationships. Conversely, traditional methods like logistic regression may be more suitable for smaller datasets, offering better interpretability and ease of use. The results indicate that systematic variable identification and the use of appropriate measurement methods can enable financial institutions to manage credit loan risk more effectively, supporting the development of sound credit policies. |
| format | Article |
| id | doaj-art-dba55d4519d2444296fc9b27b6ac32b6 |
| institution | OA Journals |
| issn | 2598-3245 2598-3288 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | P3M Politeknik Negeri Banjarmasin |
| record_format | Article |
| series | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
| spelling | doaj-art-dba55d4519d2444296fc9b27b6ac32b62025-08-20T01:57:48ZengP3M Politeknik Negeri BanjarmasinJurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer2598-32452598-32882024-12-018210011010.31961/eltikom.v8i2.11561112Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan LimitsAlgies Rifkha Fadillah0Mohamad Nurkamal Fauzan1Universitas Logistik dan Bisnis Internasional, IndonesiaUniversitas Logistik dan Bisnis Internasional, IndonesiaThis systematic literature review aims to identify key variables and measurement methods for determining maximum credit loan limits, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The complexity of setting an optimal credit limit to manage credit risk effectively presents a significant challenge. Establishing an efficient maximum loan limit is essential to mitigate credit risk, as an overly high limit increases default potential, while an excessively low limit restricts the financial institution's growth. This study identifies key variables and measurement methods, including Machine Learning techniques, Neural Networks, and traditional statistical approaches. Machine Learning models, such as Random Forest and Gradient Boosting, often surpass traditional methods in handling large, unstructured datasets due to their capacity for modeling complex, non-linear relationships. Conversely, traditional methods like logistic regression may be more suitable for smaller datasets, offering better interpretability and ease of use. The results indicate that systematic variable identification and the use of appropriate measurement methods can enable financial institutions to manage credit loan risk more effectively, supporting the development of sound credit policies.https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1156credit loanloan limitprismaslr |
| spellingShingle | Algies Rifkha Fadillah Mohamad Nurkamal Fauzan Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer credit loan loan limit prisma slr |
| title | Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits |
| title_full | Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits |
| title_fullStr | Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits |
| title_full_unstemmed | Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits |
| title_short | Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits |
| title_sort | systematic literature review identifying key variables and measuring maximum loan limits |
| topic | credit loan loan limit prisma slr |
| url | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1156 |
| work_keys_str_mv | AT algiesrifkhafadillah systematicliteraturereviewidentifyingkeyvariablesandmeasuringmaximumloanlimits AT mohamadnurkamalfauzan systematicliteraturereviewidentifyingkeyvariablesandmeasuringmaximumloanlimits |