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JCSIS

Swarm Intelligence-Enhanced Gradient Boosting Framework for Credit Default Risk Prediction in Banking Institutions.

Hakan Khan
Volume 5

Abstract

Credit default risk prediction is a fundamental task in banking institutions, as inaccurate assessment of borrower risk can lead to increased non-performing loans, capital losses, regulatory pressure, and reduced financial stability. Traditional credit scoring approaches often rely on linear assumptions and manually selected variables, which limits their ability to capture complex nonlinear relationships among borrower characteristics, repayment behavior, macroeconomic conditions, and institutional lending policies. This paper proposes a swarm intelligence-enhanced gradient boosting framework for accurate and interpretable credit default risk prediction in banking institutions. The proposed framework integrates powerful gradient boosting models, including XGBoost, LightGBM, and CatBoost, with swarm intelligence optimization algorithms such as Particle Swarm Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, and Firefly Algorithm to automatically tune critical hyperparameters and improve predictive generalization. The optimization process focuses on selecting optimal learning rates, tree depths, number of estimators, regularization coefficients, subsampling ratios, feature sampling rates, and class-weight parameters, thereby reducing reliance on manual trial-and-error configuration and conventional grid search. The framework was evaluated using large-scale banking and credit datasets containing demographic information, income level, employment status, loan amount, credit history, repayment behavior, debt-to-income ratio, collateral indicators, transaction patterns, and macro-financial variables. A comprehensive preprocessing pipeline was applied, including missing value treatment, outlier handling, categorical encoding, normalization, feature engineering, correlation analysis, and imbalance-aware sampling to address the typically low proportion of default cases in real-world credit portfolios. Experimental results demonstrate that the proposed swarm intelligence-enhanced gradient boosting framework achieves superior predictive performance compared with traditional credit scoring models, standalone gradient boosting algorithms, Support Vector Machine, Random Forest, logistic regression, and neural network baselines. The optimized framework records higher accuracy, sensitivity, specificity, F1-score, AUC-ROC, and precision-recall performance, while reducing false negatives associated with high-risk borrowers. Furthermore, SHAP-based explainability analysis was incorporated to identify the most influential default risk factors, revealing the dominant contribution of repayment history, credit utilization, debt burden, income stability, loan duration, delinquency records, and macroeconomic stress indicators. The proposed framework provides a robust, scalable, and interpretable decision-support solution for banking institutions, enabling more reliable credit approval, proactive risk monitoring, improved capital allocation, and enhanced compliance with data-driven risk management practices.

 

Keywords

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