Aug 2026· Journal of Risk and Financial Management· 0 citations· 25 references
TL;DR
By empirically proving that high-performance algorithms can be mathematically blind to demographic biases, this framework directly advances SDG 10 (Reduced Inequalities) and provides the accountable, feature-level justifications required for secure and sustainable financial inclusion (SDG 8).
Abstract
As the retail banking sector shifts toward automated lending, the black-box nature of high-performing machine learning models remains a significant barrier to regulatory transparency and institutional trust. A critical gap in existing literature is the lack of deployed frameworks that simultaneously optimize predictive accuracy, manage asymmetric financial risks, and provide actionable interpretability. To bridge this gap, this study aims to develop and evaluate a highly interpretable, ethically accountable ensemble machine learning framework for credit risk assessment. Utilizing a cross-sectional public dataset of over 45,000 generalized retail banking records, this research conducts a comprehensive comparative analysis of four diverse ensemble architectures: Bagging, Boosting, Stacking, and Voting. To address inherent class imbalance and evaluate risk tolerance, the models were integrated with Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) resampling techniques. While all architectures demonstrated high discriminative power, the SMOTE-balanced Bagging model emerged as the superior performer, achieving a peak Area Under the Curve (AUC) of 0.972 by establishing a safe operational threshold that strictly minimizes costly false approvals. Crucially, a SHapley Additive exPlanations (SHAP) framework was applied across all four models to decode their internal logic. The SHAP analysis successfully validated that the ensembles prioritize core financial behavior, such as default history and loan-to-income ratios, while correctly assigning near-zero predictive weight to demographic traits like gender and education. By empirically proving that high-performance algorithms can be mathematically blind to demographic biases, this framework directly advances SDG 10 (Reduced Inequalities). Furthermore, by resolving the performance-transparency trade-off, this study provides the accountable, feature-level justifications required for secure and sustainable financial inclusion (SDG 8).
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
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
The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability to explain model outputs in a manner auditable by supervisory bodies. This'black box'problem remains a major barrier to the adoption of Gaussian Process Regression (GPR) and related ML architectures in ICAAP and CCAR workflows despite their predictive advantages over traditional parametric approaches. This paper addresses this barrier through SHARC (SHAP for Regulatory Capital), an explainability framework for the Hybrid GPR-HS architecture and its stress-testing extension. SHapley Additive exPlanations (SHAP), derived from cooperative game theory and satisfying the properties of Local Accuracy, Missingness, Consistency, and Efficiency, are applied to Stressed Value-at-Risk (SVaR) outputs under three macro scenarios: West Asia War, Climate Risk, and AI Bubble/Regulatory Burden. SHARC decomposes SVaR into baseline, mean-driven, and volatility-driven components, enabling transparent linkage between scenario design and capital outcomes. Two findings emerge. First, SHARC consistently links non-linear SVaR outputs to underlying scenario inputs, confirming framework fidelity and providing auditable traceability of capital drivers. Second, under stress conditions, the mean return component (directional loss magnitude) dominates the variance component (volatility baseline) in determining capital levels, with implications for capital limit-setting, position management, and hedging strategy. The results establish SHARC as a regulator-aligned explainability layer that makes the Hybrid GPR-HS framework fully auditable and consistent with FRTB, ICAAP Pillar 2, and CCAR transparency requirements.
Financial resilience among high-risk retiree customer segments is a crucial issue in credit risk management, particularly because traditional scoring models are often black-box in nature and fail to provide transparent insight into the economic and behavioral factors that influence a borrower's repayment capacity. This research pursues a dual objective: first, to develop a high-performing predictive model for financial resilience classification using ensemble Machine Learning methods, and second, to apply SHAP-based Explainable AI (XAI) techniques to identify and quantify the key determinants of resilience in an accountable manner. The research methodology involved processing data from the Kaggle platform, including stratified sampling and SMOTE oversampling to address class imbalance, along with a performance comparison among Logistic Regression, Random Forest, and XGBoost. The results show that Logistic Regression proved to be the best-performing model on the held-out test set, achieving an AUC of 0.9479 and an F1-Score of 0.7313 for the resilient class. Furthermore, SHAP analysis revealed that the strongest determinants driving financial resilience were the number of prior delinquencies, loan purpose (particularly business-purpose loans), and a low debt-to-income ratio, with credit score also contributing. In practical terms, these findings provide a transparent and humane credit assessment framework, enabling financial institutions to formulate more inclusive yet prudent lending policies by prioritizing customers' actual repayment capacity over age alone.
Victor Saputra Ginting, Rahmatika Hizria, S. Takhir· Journal of Computer Networks...· 0 citations
Corporate bankruptcy early warning has often been treated as a binary classification task, yet financial distress is better understood as the outcome of interacting financial conditions that must be interpreted within practical risk-management contexts. To address this issue, this study proposes a systems-oriented explainable artificial intelligence framework for corporate bankruptcy early warning. The framework implements a Hierarchical LFRM-MACI architecture in which the Local Feature Refinement Module (LFRM) refines representations within profitability, solvency, liquidity, efficiency, and growth/shareholder performance subsystems, and cross-subsystem attention models their interactions. The framework is evaluated on the public UCI Taiwanese Bankruptcy Prediction dataset under balanced and moderately imbalanced training settings. For reporting clarity, the benchmark methods are classified into traditional machine learning methods and deep learning methods; traditional tabular learners are included in the formal single-split empirical comparison, while the proposed method’s contribution is positioned as a structured deep representation with an attribution workflow rather than as an overall superiority claim over traditional machine learning models. In the single 70%/30% validation split, the proposed model obtains ROC-AUC values of 0.9286 and 0.9269 under the 1:1.0 and 1:2.5 settings, respectively. In repeated 5-fold cross-validation with five repetitions, its ROC-AUC is 0.8940 [0.8777, 0.9103] under 1:1.0 and 0.9135 [0.9001, 0.9269] under 1:2.5. To examine the interpretability of the predictions, Permutation Feature Importance (PFI) and SHAP are applied to identify subsystem-level attribution patterns across major financial subsystems. The explanation results highlight influential predictors associated with leverage pressure, profitability and asset structure, liquidity, operating efficiency, and growth/shareholder performance. These findings indicate that explainable AI can support corporate bankruptcy early warning when predictive benchmarking is combined with transparent and auditable attribution analysis for financial decision-making.
: This study proposes an optimized ensemble framework for highly imbalanced credit card fraud detection that jointly maximizes predictive sensitivity and model interpretability. While machine learning offers significant potential for high-dimensional financial data analysis, its adoption is often hindered by extreme data sparsity and the inherent “black-box” nature of complex ensemble architectures. To bridge this gap, we develop a comprehensive pipeline incorporating six high-performance classifiers, fine-tuned via the Optuna Bayesian optimization framework. Leveraging a benchmark European dataset with a 0 . 172% fraud rate, our methodology employs a hybrid strategy of synthetic oversampling and cost-sensitive learning, integrated with SHapley Additive exPlanations (SHAP) to deconstruct complex decision boundaries. Empirical findings indicate that XGBoost delivers a superior Precision-Recall Area Under the Curve (PR-AUC) of 0 . 8755, while the Random Forest (RF) model achieves an optimal F 2 -Score (0 . 8673), effectively minimizing undetected fraudulent events. Our analysis further identifies time-of-day signals in the engineered temporal representation as relevant indicators of malicious activity. This work provides a mathematically grounded benchmarking framework for integrating Explainable Artificial Intelligence (XAI) into fraud detection pipelines, aligning high-accuracy analytics with the transparency requirements expected in regulated financial environments.
Henrique Barros, F. Antunes, Maryam Abbasi· Proceedings of the 15th Inte...· 0 citations