Aug 2026· EPRA International Journal of Economics, Business and Management Studies· pp. 285· 0 citations
TL;DR
This study benchmarks four supervised machine learning classifiers — logistic regression, random forest, gradient boosting, and extreme gradient boosting (XGBoost) — in predicting twelve-month-ahead loan delinquency using a loan-level panel drawn from commercial banks operating in an emerging Central Asian banking system over 2019–2025.
Abstract
Rising volumes of non-performing loans (NPLs) remain one of the most persistent threats to financial stability in emerging banking systems, where credit registries are thin, macroeconomic volatility is elevated, and conventional scorecards rely on linear discriminant or logistic specifications that struggle to capture non-linear interactions among borrower, loan, and macroeconomic risk factors. This study benchmarks four supervised machine learning classifiers — logistic regression, random forest, gradient boosting, and extreme gradient boosting (XGBoost) — in predicting twelve-month-ahead loan delinquency using a loan-level panel drawn from commercial banks operating in an emerging Central Asian banking system over 2019–2025. Models are trained on borrower, loan, and collateral characteristics together with a macroeconomic overlay, validated through five-fold cross-validation with chronological hold-out testing, and compared using AUC-ROC, the Gini coefficient, the Kolmogorov-Smirnov (KS) statistic, F1-score, and Brier score. The results show that tree-based ensembles substantially outperform the logistic baseline, with XGBoost achieving the highest discriminatory power, and that augmenting loan-level features with macroeconomic overlay variables materially improves out-of-sample predictive accuracy. Feature-importance analysis identifies delinquency history, debt-service-to-income ratio, collateral coverage, and sectoral exposure as the dominant predictors. The findings offer commercial banks and supervisory authorities in emerging markets an empirically validated architecture for early-warning credit scoring capable of narrowing the gap between reported and risk-based measures of asset quality.
Banking crises are a persistent threat to macroeconomic stability in emerging markets, where conventional econometric monitoring frameworks often fail to capture non-linear macro-financial relationships. This paper examines whether machine learning algorithms can improve the detection of banking crisis risk in Nigeria compared to standard logistic regression. We compare the performance of Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) against logistic regression using annual data from the African Financial Crises dataset (1954–2014). Resampling is only implemented on the training set to overcome the infrequency of crisis events. Performance on models is assessed based on accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) in a rigorous out-of-time validation setting. Our findings indicate that tree-based ensemble models outperform logistic regression on the test set: XGBoost achieves the best generalization performance (AUC = 1.0; F1 = 0.95 in non-crisis, 0.80 in crisis), whereas Random Forest has the highest cross-validated F1-score on the training set. The most important variables are exchange rate volatility, inflation, and indicators of systemic crisis. The most significant crisis indicators are, however, seen in crisis years, which means that the annual data do not provide much lead-time to detect the crisis. These results should be taken with caution because of the small sample size and the limited number of crisis observations during the test period. Altogether, machine learning models have potential as additional tools to monitor banking crises in Nigeria, though at the moment they are not fully operational as policy instruments.
Ntanganedzeni Mandiwana, Thakhani Ravele, C. Sigauke et al.· Analytics· 0 citations
Predicting credit risk is vital for banks as it safeguards financial stability, minimizes default losses, optimizes capital, and ensures regulatory compliance. This study aims to predict credit risk (High/Low) in commercial banks by integrating machine learning with traditional econometric approaches. The Structural Learning in Vague Environments (SLAVE) fuzzy rule-based model handles ambiguity in financial decisions, while the eXtreme Gradient Boosting (XGBoost) uncovers non-linear patterns among predictors. Input variables—profitability, liquidity risk, ESG (environmental, social, and governance) score, and monetary freedom—were selected via multicollinearity tests and three panel regression models, including ordinary least squares (OLS), fixed effects, and random effects models. The empirical investigation uses a panel dataset of forty commercial banks across seven Middle Eastern countries from 2014 to 2023, yielding 400 observations. Regression results reveal that profitability and ESG score significantly reduce credit risk. Liquidity risk and monetary freedom increase credit risk. XGBoost combined with the SHapley Additive exPlanations (SHAP)-based interpretation identifies ESG Score as the most influential predictor. The SLAVE model was evaluated using three data splits: 70/30, 80/20, and 90/10. The 80/20 split achieved the highest accuracy, with superior performance in identifying low-risk banks. Stronger ESG performance and stable monetary environments contribute to fostering sustainable banking and reducing credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.
Jamil J. Jaber, A. A. Alkhawaldeh, Qusay Ayman Sulayman Mazahreh et al.· Risks· 0 citations
Non-Performing Loans (NPL) are a fundamental indicator of a financial institution's asset health, reflecting loans that fail to meet interest or principal payment obligations as agreed. A high NPL ratio negatively impacts a bank's financial performance, such as decreased profitability as measured by Return on Assets (ROA) and decreased liquidity. Bank Indonesia sets an NPL tolerance limit of 5% of total credit provided by banking financial institutions. Therefore, a predictive model is needed that can detect the possibility of customers experiencing NPLs early. This study aims to identify relevant factors in predicting NPLs and create an NPL prediction model based on these factors. The contribution of this study lies in combining the results of three feature selection techniques: Chi-Square, Mutual Information, and Random Forest feature importance, using the average score eliminated by the Recursive Feature Elimination technique. Several ensemble algorithms, namely Random Forest, XGBoost, Gradient Boosting, and LightGBM, were explored to produce the best-performing model. Then, hyperparameter tuning was performed on the best model. The Random Forest model produced the best performance, with 92.17% accuracy, 78.1% precision, 98.1% recall, and 95.5% AUC. Hyperparameter tuning was shown to improve recall, thus improving the model's ability to measure how much positive data (Current class) was successfully predicted by the model. The results of this study can assist management in making credit decisions. Thus, it is hoped that it can help reduce the number of NPL cases.
David Jefri Aruan, Rusdah Rusdah, Ahmad Pudoli· IDEALIS : InDonEsiA journaL...· 0 citations
Credit risk in business-to-business (B2B) transactions can threaten financial stability if it is not managed effectively. This study evaluates the use of machine learning (ML) methods to predict credit risk in B2B transactions, using 4,828 observations from large companies in Bosnia and Herzegovina over a five-year period. Three ensemble-based ML models (Bagging Decision Tree, Random Forest, and Gradient Boosting) were compared with logistic regression. All ML models showed strong predictive performance, with Gradient Boosting performing slightly better overall. Liquidity, activity, and leverage indicators were the most important predictors across all models, while non-financial variables made only a limited contribution. The findings highlight the importance of accounting information in credit risk assessment and are relevant to IFRS 9, where probability of default is a key input to expected credit loss estimation under the general approach. These results provide a basis for further development of credit risk models for non-financial companies.
Suzi Mikulić· Ekonomska Misao i Praksa· 0 citations
An Explainable Machine Learning (XML) framework for credit risk assessment that combines an ensemble classifier, integrating XGBoost, Random Forest, and LightGBM, with an integrated SHAP-and-LIME explainability layer is proposed and evaluated using a large-scale retail and priority-sector loan dataset drawn from public sector, private sector, regional rural, and small finance bank segments operating in India.
A. Agrawal, Vaibhav C. Gandhi· International journal of com...· 0 citations
An AI-based credit scoring approach that uses machine learning, deep learning, and hybrid models to improve accuracy and scalability is proposed, concluding that AI-driven credit scoring enhances smart banking, customer experience, and financial inclusion.
L. O'Connor· International Journal of Art...· 0 citations