Published on: May 2026
AN INTERPRETABLE MACHINE LEARNING FRAMEWORK FOR LOAN DEFAULT RISK PREDICTION IN FINANCIAL SYSTEMS
Ramya P Pruthvi D M Nandini G V Nayana H N
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Abstract
Three well‐established classifiers—Logistic Regression, Random Forest, and XGBoost—were trained and evaluated across a standard set of performance indicators: overall accuracy, area under the receiver operating characteristic curve (ROC‐AUC), precision, recall, and F1‐score. XGBoost delivered the strongest accuracy by leveraging its gradient‐boosting architecture to capture nonlinear interactions within the data. Complementing the predictive component, an explainability layer based on SHapley Additive exPlanations (SHAP) was integrated to surface the relative contribution of each input variable to individual predictions, thereby converting an otherwise opaque ensemble into a transparent decision‐support tool.
The resulting framework strikes a deliberate balance between predictive power and model transparency—a pairing that is particularly valuable in regulated credit‐risk environments where decisions must be both accurate and justifiable.
Keywords: Loan Default Prediction, XGBoost, Random Forest, Logistic Regression, SMOTE, SHAP, Credit Risk, Interpretable Machine Learning
How to Cite this Paper
P, R., M, P. D., V, N. G. & N, N. H. (2026). An Interpretable Machine Learning Framework for Loan Default Risk Prediction in Financial Systems. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.265
P, Ramya, et al.. "An Interpretable Machine Learning Framework for Loan Default Risk Prediction in Financial Systems." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.265.
P, Ramya,Pruthvi M,Nandini V, and Nayana N. "An Interpretable Machine Learning Framework for Loan Default Risk Prediction in Financial Systems." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.265.
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- •Published on: May 08 2026
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