Predictive risk assessment credit scoring using supervised learning
This study explores the application of supervised learning models within credit scoring, aiming to revolutionize risk assessment in lending decisions. The primary goal involves comparing these advanced methodologies against conventional credit assessment techniques to ascertain their effectiveness i...
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| Format: | Final Year Project / Dissertation / Thesis |
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2025
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| Online Access: | http://eprints.utar.edu.my/6123/1/fyp_DE_2025_KWH.pdf http://eprints.utar.edu.my/6123/ |
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| _version_ | 1848452682151362560 |
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| author | Khor, Wei Heng |
| author_facet | Khor, Wei Heng |
| author_sort | Khor, Wei Heng |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This study explores the application of supervised learning models within credit scoring, aiming to revolutionize risk assessment in lending decisions. The primary goal involves comparing these advanced methodologies against conventional credit assessment techniques to ascertain their effectiveness in determining creditworthiness. In response to the escalating complexity of financial transactions and the wealth of available data, this research seeks to elevate the precision and efficiency of credit risk evaluation. Supervised learning, known for its ability to learn from labelled datasets, presents an opportunity to redefine credit scoring by leveraging historical credit information.
The core focus is on assessing the predictive capabilities of supervised learning algorithms—specifically Logistic Regression, Random Forest, K-Nearest Neighbours, Support Vector Machines and Gradient Boosting—against established credit scoring methods. By harnessing the power of these modern techniques and analysing intricate credit patterns, this research endeavours to deliver more accurate credit risk assessments. It strives to surpass the existing industry norms by using machine learning models to refine credit evaluation processes. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.6123 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.61232025-11-05T12:09:40Z Predictive risk assessment credit scoring using supervised learning Khor, Wei Heng T Technology (General) TD Environmental technology. Sanitary engineering This study explores the application of supervised learning models within credit scoring, aiming to revolutionize risk assessment in lending decisions. The primary goal involves comparing these advanced methodologies against conventional credit assessment techniques to ascertain their effectiveness in determining creditworthiness. In response to the escalating complexity of financial transactions and the wealth of available data, this research seeks to elevate the precision and efficiency of credit risk evaluation. Supervised learning, known for its ability to learn from labelled datasets, presents an opportunity to redefine credit scoring by leveraging historical credit information. The core focus is on assessing the predictive capabilities of supervised learning algorithms—specifically Logistic Regression, Random Forest, K-Nearest Neighbours, Support Vector Machines and Gradient Boosting—against established credit scoring methods. By harnessing the power of these modern techniques and analysing intricate credit patterns, this research endeavours to deliver more accurate credit risk assessments. It strives to surpass the existing industry norms by using machine learning models to refine credit evaluation processes. 2025-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6123/1/fyp_DE_2025_KWH.pdf Khor, Wei Heng (2025) Predictive risk assessment credit scoring using supervised learning. Final Year Project, UTAR. http://eprints.utar.edu.my/6123/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering Khor, Wei Heng Predictive risk assessment credit scoring using supervised learning |
| title | Predictive risk assessment credit scoring using supervised learning |
| title_full | Predictive risk assessment credit scoring using supervised learning |
| title_fullStr | Predictive risk assessment credit scoring using supervised learning |
| title_full_unstemmed | Predictive risk assessment credit scoring using supervised learning |
| title_short | Predictive risk assessment credit scoring using supervised learning |
| title_sort | predictive risk assessment credit scoring using supervised learning |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6123/1/fyp_DE_2025_KWH.pdf http://eprints.utar.edu.my/6123/ |
| url_provider | http://eprints.utar.edu.my |