Jul 2026· Advances in Economics, Management and Political Sciences· 0 citations
TL;DR
The discussion shows that ensemble methods can provide strong discrimination across public credit datasets, while the usefulness of a model also depends on whether its outputs can be audited and communicated.
Abstract
Credit scoring has become more data-driven as online lending platforms collect larger and more varied borrower records. Machine learning methods can model nonlinear patterns that traditional scorecards often miss, but their decisions are harder to explain in a regulated lending environment. This paper discusses how explainable artificial intelligence can be used to make credit scoring models more transparent, with a focus on SHAP and LIME. Using the Lending Club dataset and recent empirical evidence from credit-risk studies, the paper compares the predictive role of ensemble learning models with the interpretive roles of SHAP and LIME. The discussion shows that ensemble methods can provide strong discrimination across public credit datasets, while the usefulness of a model also depends on whether its outputs can be audited and communicated. SHAP is better suited to global feature analysis, model review, and risk-policy design. LIME is more useful when a single loan decision must be explained to staff or customers. Used together, the two methods offer a practical route to balance accuracy, transparency, and compliance in credit scoring.
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
Banking is increasingly shaped by expanding data volumes, more complex borrower behaviour, and stricter credit risk management requirements. Under such conditions, scoring models are becoming especially relevant as instruments for the formalised assessment of creditworthiness, combining analytical accuracy, speed of decision-making, and the possibility of integration into the bank’s risk management system. The study compares traditional and modern scoring models in bank credit risk management and proposes an approach to their practical use in Ukrainian banking. Its focus is on scoring models as instruments for credit risk assessment. The study combines comparative analysis, matrix modelling, simulation, statistical modelling, and machine learning methods. Given limited access to primary banking information and confidentiality requirements, the empirical analysis was conducted on a synthesised demonstration dataset designed to reflect the structure of a real retail credit portfolio. For the analysis, a sample of 1,000 observations with a default share of 22.0% was constructed, and logistic regression, discriminant analysis, Random Forest, XGBoost, and a hybrid logit + ML re-ranking model were used for comparison. The results showed that XGBoost provided the highest predictive accuracy, with an AUC-ROC of 0.861, Gini of 0.722, Recall of 0.781, and Brier score of 0.141, whereas logistic regression demonstrated an AUC-ROC of 0.781 and retained advantages in terms of interpretability and suitability for validation. The hybrid model achieved an AUC-ROC of 0.848, Gini of 0.696, Recall of 0.773, and Brier score of 0.144, thus ensuring the best balance between accuracy, explainability, calibration, and practical applicability. Practically, the study offers an adaptive approach to selecting scoring models and a matrix for evaluating them under Ukrainian banking conditions, taking into account the requirements of the regulatory environment, data quality, and the instability of the operating conditions of Ukrainian banks.
With the rapid growth of online lending platforms, credit risk management has become increasingly important. Aligned with Basel Committee recommendations, this study proposes a two-stage hybrid framework that integrates internal default prediction models with external credit ratings at the decision-making level. Unlike prior studies that either rely solely on internal models or treat external ratings as input features, the proposed framework preserves the distinct strengths of both sources. In the first stage, twelve machine learning models are combined with five data balancing techniques and feature selection, yielding 60 distinct configurations evaluated under class imbalance. Performance is assessed using conventional metrics (F1, G-mean, AUC) and a profit-based metric (Profit_Score) that reflects the economic impact of model decisions by quantifying avoided losses and forgone revenues. Logistic regression with random oversampling is selected as the optimal model. The key methodological contribution lies in the second stage, where a dynamic credit rating adjustment mechanism is introduced based on a composite score integrating predicted default probability, external credit rating, and loan amount. Results show that the dynamic approach outperforms both the static strategy (by 14.55%) and the standalone internal model (by 30.4%). The findings demonstrate that decision-level integration of internal and external models, and addressing class imbalance, enhances both predictive performance and profitability.
P. Khalili, Mehrdad Kargari, Mohammad Ali Rastegar et al.· Scientific Reports· 0 citations
It is argued that predictive accuracy and regulatory transparency are not competing objectives but complementary necessities for institutional survival in Nepal’s cooperative sector.
S. K. Sahani, Tsair-Fwu Lee, Digvijay Pandey et al.· Journal of Intelligent Decis...· 0 citations
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.
