Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumaraswamy, M.; Christina, Sowmya; Devi, A Uma; Varikunta, Obulseu; Basha, Md Shaik Amzad Title A name given to the resource Smote-Enhanced Machine Learning Approaches to Banking Loan Default Prediction: a Multi-Model Study Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 4th International Conference for Advancement in Technology, ICONAT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICONAT66879.2025.11362377" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONAT66879.2025.11362377</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033879390?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033879390?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumaraswamy M., Vignan's Foundation for Science and Technology Deemed to be University VFSTR, Department of Management Studies, Guntur, India; Christina S., Christ (Deemed to be University), Department of Professional Studies, Bengaluru, India; Devi A.U., R.M.K. Engineering College, Department of Management Studies, Chennai, India; Varikunta O., College of Management &amp; Computer Applications, MIT-ADT University, Pune, India; Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Hyderabad, India Description An account of the resource Accurate prediction of loan defaults is vital for banking risk management, yet loan dataset suffer severe class imbalance, with charged-off loans representing typically less than 10 % of all cases Models trained on such data often exhibit high overall accuracy but poor recall for defaults, limiting their We utilized a stratified 80 / 20 train-test split on a loan dataset dataset of 209,715 loans and 29 features, standardizing numeric variables and one-hot encoding categoricals. Ten algorithmsincluding Logistic Regression, Decision Tree, Random Forest, XGBoost, LightGBM, CatBoost, SGD, MLP, GaussianNB, and KNN were trained without resampling. To address imbalance, we applied SMOTE to the training set, generating synthetic minority instances via k-nearest neighbor interpolation. Baseline models achieved ? 92 % accuracy but recall for defaults ranged 0.04-0.53, underscoring poor minority detection. SMOTE-augmented models saw recall increases up to +0.52 (e.g., KNN: 0.04 ? 0.56) at the cost of reduced accuracy and slight AUC declines, highlighting a precision-recall trade-off. Our systematic multi-model framework demonstrates that SMOTE-enhanced Logistic Regression and KNN markedly improve default recall, offering banks actionable options to prioritize risk detection, while tree-based ensembles retain high ranking performance for applications emphasizing overall accuracy and ROC AUC. 2025 IEEE. Subject The topic of the resource Banking; Credit Risk Assessment; Loan Default; Machine Learning; SMOTE Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159573-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online