Advancing Loan Approval Prediction With SHAP‐Guided Feature Selection and LIME‐Based Model Interpretability in a Multiclassifier Context Through a Web‐Based Application Development Approach.
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| Title: | Advancing Loan Approval Prediction With SHAP‐Guided Feature Selection and LIME‐Based Model Interpretability in a Multiclassifier Context Through a Web‐Based Application Development Approach. |
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| Authors: | Akter, Raisa1 (AUTHOR), Halder, Rajib Kumar1 (AUTHOR) rajib.cse1346@gmail.com, Uddin, Mohammed Nasir2 (AUTHOR), Uddin, Md. Ashraf3 (AUTHOR), Khraisat, Ansam3 (AUTHOR), Rahman, Mijanur1 (AUTHOR), Hossain, Md. Kabir4 (AUTHOR), Murray, Richard (AUTHOR) rmurray@wiley.com |
| Source: | International Journal of Intelligent Systems. 1/6/2026, Vol. 2026, p1-14. 14p. |
| Subjects: | Feature selection, Web-based user interfaces, Data integration, Ensemble learning |
| Abstract: | In today's dynamic financial environment, bank loan approval systems are crucial for determining credit accessibility and maintaining economic stability. Efficient and accurate mechanisms help financial institutions minimize risks, enhance customer satisfaction, and make informed lending decisions. Traditional evaluation methods, however, often struggle with complex applicant data, underscoring the need for advanced, data‐driven approaches. This study proposes an enhanced loan approval prediction framework that integrates SHAP‐guided feature selection and LIME‐based interpretability within a robust multiclassifier architecture. The methodology includes extensive data preprocessing, handling missing values, and encoding categorical variables, followed by SHAP to identify the most influential features. Using two Kaggle datasets, logistic regression achieved the highest performance, with 86.17% accuracy and 81% AUC on Dataset 1 and 99.06% accuracy on Dataset 2. LIME provided intuitive, visual explanations of model predictions, fostering transparency and trust. In addition, a user‐friendly, real‐time web application was developed for practical deployment. Overall, the study advances intelligent, interpretable, and efficient loan approval systems for modern banking. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 190687627 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advancing Loan Approval Prediction With SHAP‐Guided Feature Selection and LIME‐Based Model Interpretability in a Multiclassifier Context Through a Web‐Based Application Development Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Akter%2C+Raisa%22">Akter, Raisa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Halder%2C+Rajib+Kumar%22">Halder, Rajib Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rajib.cse1346@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Uddin%2C+Mohammed+Nasir%22">Uddin, Mohammed Nasir</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Uddin%2C+Md%2E+Ashraf%22">Uddin, Md. Ashraf</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Khraisat%2C+Ansam%22">Khraisat, Ansam</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rahman%2C+Mijanur%22">Rahman, Mijanur</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hossain%2C+Md%2E+Kabir%22">Hossain, Md. Kabir</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Murray%2C+Richard%22">Murray, Richard</searchLink> (AUTHOR)<i> rmurray@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 1/6/2026, Vol. 2026, p1-14. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Web-based+user+interfaces%22">Web-based user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In today's dynamic financial environment, bank loan approval systems are crucial for determining credit accessibility and maintaining economic stability. Efficient and accurate mechanisms help financial institutions minimize risks, enhance customer satisfaction, and make informed lending decisions. Traditional evaluation methods, however, often struggle with complex applicant data, underscoring the need for advanced, data‐driven approaches. This study proposes an enhanced loan approval prediction framework that integrates SHAP‐guided feature selection and LIME‐based interpretability within a robust multiclassifier architecture. The methodology includes extensive data preprocessing, handling missing values, and encoding categorical variables, followed by SHAP to identify the most influential features. Using two Kaggle datasets, logistic regression achieved the highest performance, with 86.17% accuracy and 81% AUC on Dataset 1 and 99.06% accuracy on Dataset 2. LIME provided intuitive, visual explanations of model predictions, fostering transparency and trust. In addition, a user‐friendly, real‐time web application was developed for practical deployment. Overall, the study advances intelligent, interpretable, and efficient loan approval systems for modern banking. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/int/8899164 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Web-based user interfaces Type: general – SubjectFull: Data integration Type: general – SubjectFull: Ensemble learning Type: general Titles: – TitleFull: Advancing Loan Approval Prediction With SHAP‐Guided Feature Selection and LIME‐Based Model Interpretability in a Multiclassifier Context Through a Web‐Based Application Development Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Akter, Raisa – PersonEntity: Name: NameFull: Halder, Rajib Kumar – PersonEntity: Name: NameFull: Uddin, Mohammed Nasir – PersonEntity: Name: NameFull: Uddin, Md. Ashraf – PersonEntity: Name: NameFull: Khraisat, Ansam – PersonEntity: Name: NameFull: Rahman, Mijanur – PersonEntity: Name: NameFull: Hossain, Md. Kabir – PersonEntity: Name: NameFull: Murray, Richard IsPartOfRelationships: – BibEntity: Dates: – D: 06 M: 01 Text: 1/6/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08848173 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Journal of Intelligent Systems Type: main |
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