Explaining Credit Scoring Models in Digital Lending: A Comparison of SHAP and LIME

An Interpretability Analysis of Credit Default Prediction Using Random Forest with SHAP and LIME

Credit default prediction has become an important application of machine learning in the banking and financial sector, as it helps financial institutions identify potential loan defaulters and support informed lending decisions. Although machine learning models often provide high predictive performance, many of them function as black-box system, making it difficult for financial analysts and decision-makers to understand the reasoning behind their predictions. This lack of transparency can reduce user trust, particularly in high-stakes financial applications where explainable decisions are essential. To address this challenge, this study explores the use of Explainable Artificial intelligence (XAI) techniques to improve the interpretability of credit default prediction. A Random Forest classifier was developed using a publicly available credit default dataset containing financial attributes such as employment status, bank balance, annual salary, and loan default status. The dataset was preprocessed and partitioned into training and testing sets before model development. To explain the prediction process, SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) were integrated with the trained Random Forest model. SHAP was used to provide both global and local explanations by identifying the overall importance and contribution of individual features, while LIME generated instance-level explanations to illustrate how specific features influenced individual predictions. The explanation results were presented through visualizations, including feature importance plots, waterfall plots, and local explanation graphs, allowing a clearer understanding of the model's decision-making process. The findings demonstrate that the combined use of SHAP and LIME enhances the transparency and interpretability of the Random Forest model by providing complementary perspectives on feature contributions. This study 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.

CREDIT SCORING MODELS IN BANK CREDIT RISK MANAGEMENT: COMPARATIVE ASSESSMENT AND APPLICATION IN UKRAINIAN BANKING PRACTICE

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.

A two‑stage hybrid framework for default prediction in digital lending through integrating internal and external credit models.

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.

The Effect of Data Interventions on Local Explanations in Credit Risk Models

Explainable Machine Learning for Credit Risk Management and Intelligent Lending Decisions in Nepalese Cooperative Banks: A Mathematical Review

It is argued that predictive accuracy and regulatory transparency are not competing objectives but complementary necessities for institutional survival in Nepal’s cooperative sector.

A Comparative Study of Traditional Statistical Models and Machine Learning Algorithms in Credit Risk Assessment

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.