ItemConference Paper

Smote-Enhanced Machine Learning Approaches to Banking Loan Default Prediction: a Multi-Model Study

Title
Smote-Enhanced Machine Learning Approaches to Banking Loan Default Prediction: a Multi-Model Study
Creator
Kumaraswamy, M.; Christina, Sowmya; Devi, A Uma; Varikunta, Obulseu; Basha, Md Shaik Amzad
Description
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.
Source
2025 IEEE 4th International Conference for Advancement in Technology, ICONAT 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Banking; Credit Risk Assessment; Loan Default; Machine Learning; SMOTE
Coverage
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 & 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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833159573-9;
Format
online
Language
English
Type
Conference paper

Citation

Kumaraswamy, M.; Christina, Sowmya; Devi, A Uma; Varikunta, Obulseu; Basha, Md Shaik Amzad, “Smote-Enhanced Machine Learning Approaches to Banking Loan Default Prediction: a Multi-Model Study,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26073.

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