{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T10:29:25Z","timestamp":1771324165392,"version":"3.50.1"},"reference-count":37,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001778","name":"Deakin University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001778","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019381","name":"Jagannath University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>In today\u2019s 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\u2010driven approaches. This study proposes an enhanced loan approval prediction framework that integrates SHAP\u2010guided feature selection and LIME\u2010based 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\u2010friendly, real\u2010time web application was developed for practical deployment. Overall, the study advances intelligent, interpretable, and efficient loan approval systems for modern banking.<\/jats:p>","DOI":"10.1155\/int\/8899164","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T17:24:59Z","timestamp":1767720299000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Loan Approval Prediction With SHAP\u2010Guided Feature Selection and LIME\u2010Based Model Interpretability in a Multiclassifier Context Through a Web\u2010Based Application Development Approach"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2526-8393","authenticated-orcid":false,"given":"Raisa","family":"Akter","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8542-2258","authenticated-orcid":false,"given":"Rajib Kumar","family":"Halder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0365-294X","authenticated-orcid":false,"given":"Mohammed Nasir","family":"Uddin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4316-4975","authenticated-orcid":false,"given":"Md. 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