It is suggested that superior ranking performance does not necessarily imply superior decision quality and that effective credit risk modeling requires balancing predictive flexibility with probabilistic reliability and governance stability.
Abstract
Credit risk assessment underpins lending decisions, pricing strategies, portfolio management, and regulatory capital allocation within modern financial systems. Logistic regression has historically served as the dominant modeling framework in credit scoring due to its probabilistic coherence and interpretability. In recent years, advances in machine learning—particularly tree-based ensemble methods such as Random Forest and Gradient Boosting—have demonstrated strong predictive performance and often outperform traditional approaches in discrimination metrics such as the area under the ROC curve (AUC). However, the adoption of machine learning in credit risk modeling remains debated due to concerns regarding probability calibration, temporal robustness, interpretability, and regulatory governance. This paper provides a comprehensive comparison of traditional statistical models and tree-based machine learning approaches in credit risk assessment. Rather than focusing exclusively on discriminatory performance, the analysis adopts a multidimensional evaluation framework incorporating calibration quality and temporal stability. Drawing on foundational theory and recent empirical evidence, the paper argues that model adequacy in credit risk is inherently context dependent. A three-pillar framework—discrimination, calibration, and temporal robustness—is proposed to guide academic research and practical model deployment. The findings suggest that superior ranking performance does not necessarily imply superior decision quality and that effective credit risk modeling requires balancing predictive flexibility with probabilistic reliability and governance stability.
A systematic literature review of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026, synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operational deployment.
Bolun Zhang, Jun Luo, Ruobing Wu et al.· Journal of Risk and Financia...· 0 citations
The paper shows that ensemble learning models have superior predictive power and argues that behavioral data complement traditional datasets for underbanked populations, such as "credit invisibles," and makes a strong case for XAI being essential for model transparency, combating bias, and meeting regulatory requirements.
Bojun Chen· Advances in Economics, Manag...· 0 citations
This study addresses the critical challenge of credit risk assessment in data-scarce emerging and frontier markets by developing and empirically validating an enhanced eight-criterion (8C) cash-flow-centric credit scoring framework. Traditional default prediction models rely heavily on standardized financial statements and transparent accounting systems—conditions that are often absent in fragile institutional environments such as Yemen, where information asymmetry and institutional weaknesses complicate objective credit evaluation. Using a unique hand-collected dataset of 40 complete corporate credit files from the Cooperative and Agricultural Credit Bank (2010–2019), this study conducts a comparative empirical analysis between the incumbent 7C heuristic framework and the proposed 8C model. The methodology employs linear discriminant analysis and binary logistic regression to evaluate predictive accuracy, classification stability, and explanatory power within a small-sample frontier setting. The results demonstrate that the 8C model substantially outperforms the traditional 7C approach, achieving an overall classification accuracy of 92.5% compared to 77.5%, while reducing False Negative (false acceptance of defaulters) from 33.3% to 11.1%. The area under the ROC curve (AUC) increases from 0.74 to 0.94, indicating strong discriminatory power and improved risk differentiation capacity. Logistic regression results confirm that cash-flow-based repayment capacity and borrower character are the strongest predictors of default, whereas collateral shows no statistically significant explanatory power in this frontier context. The findings provide empirical support for shifting from collateral-heavy lending practices toward forward-looking, cash-flow-sensitive underwriting models aligned with IFRS 9 and Basel III principles. The study contributes theoretically, methodologically, and practically by offering a ready-to-implement framework specifically designed for data-constrained frontier banking systems and institutional environments characterized by limited financial transparency.
Waleed Yahya Mohsen Mohammed Al-Sabri· Multidisciplinary Science Jo...· 0 citations
This review evaluates accuracy, stability and interpretability, offering guidance for model selection in real-world credit scoring, and Logistic regression remains essential in regulated contexts requiring transparency, supporting informed decisions on balancing performance and explainability.
Radouane Aboulmaouda, Khadija Slimani, N. Chaoui· Journal of Risk and Financia...· 0 citations
This study explores the use of Explainable Artificial intelligence techniques to improve the interpretability of credit default prediction and highlights the practical value of explainable machine learning in developing more understandable, trustworthy, and accountable credit risk assessment systems for real-world financial decision-making.
Muskan, B. Sidhu· International Journal of Com...· 0 citations
Credit risk assessment is pivotal to the sustainability of micro-lending institutions, particularly in emerging economies such as Ghana, where conventional evaluation methods remain predominantly manual and subjective. Traditional approaches, which rely on face-to-face interviews, personal judgments, and simple background checks, are vulnerable to human biases, inconsistencies, and inefficiencies that contribute to elevated default rates and broader financial instability. This study investigates the application of machine learning (ML) techniques, specifically Random Forest (RF), Extra Tree Classifier (ETC), and a probability-averaged Ensemble Classifier, to enhance credit risk assessment in Ghanaian micro-lending institutions. Using a quantitative experimental research design, the study analysed 32,581 loan records drawn from Tepa Man Microfinance Institution. Data preprocessing included missing-value imputation, one-hot encoding, and class balancing via random oversampling, applied exclusively to the training set. Model performance was evaluated through 10-fold stratified cross-validation using accuracy, precision, recall, F1-score, AUC-ROC, Cohen's Kappa, and Matthews Correlation Coefficient (MCC). Hyperparameters were set to scikit-learn defaults (n_estimators = 100, random_state = 42) to ensure reproducibility. The Random Forest and Extra Tree Classifiers each achieved a mean accuracy of 99.33% and an AUC-ROC of 0.9997, results that are consistent with the high-quality, real-world dataset and are critically interpreted in the context of potential overfitting risks. Feature importance analysis identified the loan-to-income ratio and interest rate as the dominant predictors of default. The Ensemble Method, which averages class probabilities across both base models, achieved 84.25% accuracy and an AUC of 0.9231, demonstrating stronger generalization than the individual classifiers. The study concludes that integrating ML models can substantially improve the accuracy, consistency, and reliability of credit risk evaluations, thereby reducing default rates and supporting financial inclusion in Ghana's microfinance sector.
P. Addo, Samuel Kofi Akpatsa, Emmanuel Mensah et al.· Journal of Applied Social Sc...· 0 citations