2026· EPJ Web of Conferences· 0 citations· 11 references
Abstract
SME credit files arrive incomplete, and the gaps leave room for anchoring, confirmation bias and loss aversion. We compare human, algorithmic and hybrid credit decisions using a benchmark credit dataset alongside a vignette experiment with credit analysts working in Morocco's Souss-Massa region. The modelling arm pairs L2-regularised logistic regression with gradient-boosted trees, adding stratified validation, calibration analysis, SHAP and LIME. In the human arm, matched cases vary the requested amount while everything else is held constant. Analysts were least stable on borderline files, and their decisions moved with the anchor. The boosted model held steadier but leaned harder on indicators that track how thick a file is. AI-first assistance improved consistency and deepened deference to the model; human-first assistance preserved contextual overrides; explanation-gating struck the best balance, though only where SHAP and LIME agreed. We assess distribution through demographic-parity difference, disparate-impact ratio, equal-opportunity difference and false-positive-rate difference. What the results support is a governed hybrid: weak explanations withheld, overrides auditable, human review genuinely available. A regional sample and benchmark data bound how far any of these travels.
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).
Htet Nge Nge Ko, Aung Htoo Khine, Shadab Kalhoro et al.· Journal of Risk and Financia...· 0 citations
The cooperative banking sector in Nepal constitutes a foundational pillar of financial inclusion, serving approximately 7.4 million members across 31,450 primary cooperatives and disbursing loans exceeding NPR 453 billion. Yet this sector is hemorrhaging credibility. The National Cooperative Bank Limited reported a non-performing loan ratio of 33.01 percent as of mid-July 2025, with its capital adequacy ratio collapsing to 0.82 percent — a figure that would trigger immediate regulatory intervention in any conventional banking jurisdiction. Against this backdrop, the question is no longer whether cooperative banks in Nepal need better risk assessment tools, but whether they can afford to continue without them.
This paper develops a comprehensive theoretical framework for integrating explainable machine learning into credit risk management systems of Nepalese cooperative banks. We derive the complete mathematical architecture of ensemble gradient boosting — specifically XGBoost and LightGBM — alongside post-hoc interpretability mechanisms grounded in cooperative game theory, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). The framework incorporates information-theoretic feature selection, SMOTE-based class imbalance correction, fairness constraints calibrated to Nepal’s socio-economic heterogeneity, and a rigorous convergence analysis of the boosting iteration. We prove consistency of the mutual-information feature selector, derive the bias-variance decomposition for gradient-boosted ensembles, establish generalization bounds via Rademacher complexity, and verify the four Shapley axioms for the TreeSHAP algorithm. Drawing upon Nepal Rastra Bank’s Financial Stability Report FY 2024/25, the National Cooperative Federation of Nepal’s sectoral statistics, and the Government of Nepal’s Economic Survey 2025/26, we situate the technical apparatus within Nepal’s regulatory architecture — the Cooperative Act 2017, the Financial Sector Development Strategy 2022–2026, and the National Financial Inclusion Roadmap. The paper argues 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
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.
Zekun Li· Advances in Economics, Manag...· 0 citations
Real-time retail credit scoring is a data-intensive cognitive computing task. Each decision must fuse heterogeneous signals, execute a non-linear model, return a calibrated probability of default (PD), and emit a regulator-compliant local explanation within milliseconds. We address the most demanding segment of unsecured lending in Kazakhstan—Salary-Project-Independent (SPI) borrowers, whose principal income stream is not observable by the lender—and frame scoring as a constrained optimisation problem where we maximise discrimination subject to interpretability, latency, and calibration constraints. We propose a tenure-stratified hybrid framework that couples (i) an online weight-of-evidence logistic regression (WOE-LR) scorecard with (ii) an offline self-attention stacked ensemble (LightGBM, CatBoost, and a tabular self-attention network) whose calibrated PD is quantile-binned, WOE-encoded, and re-injected into the online scorecard as a single auditable predictor. On 551,962 production contracts that originated in 2022–2024, the repeat-client hybrid attains an area under the receiver operating characteristic curve (AUROC) of 0.826, a Gini coefficient of 0.65, and a Kolmogorov–Smirnov (KS) statistic of 0.495, preserving roughly half of the offline ensemble’s lift over the linear baseline (AUROC 0.79→0.897) while retaining a fully auditable twelve-coefficient scorecard in production. The new-client scorecard attains an AUROC of 0.741. Non-parametric isotonic recalibration reduces the expected calibration error from 0.27 to below 0.01 and raises the Hosmer–Lemeshow p-value above 0.99 without altering discrimination. The framework complies with the model risk standards of the Agency of the Republic of Kazakhstan for Regulation and Development of the Financial Market and is delivered as a Spark/MLOps reference architecture, illustrating how big data engineering, attention-based representation learning, and post hoc explanations can be co-designed for a high-stakes, high-throughput, regulated AI application.
Gulnaz Zakariya, A. Moldagulova, Nor’ashikin Ali· Big Data and Cognitive Compu...· 0 citations
Payroll-anchored retail borrowers—individuals whose monthly remuneration is routed into an account at the lending institution through a salary-project arrangement—constitute the volume backbone of unsecured consumer lending in Kazakhstan, generating the largest origination flow, the lowest realized default rate, and the majority of the systemic regulatory and capital sensitivities of second-tier banks. Payroll anchoring also changes the lender’s information set, which motivates a study of how that advantage translates into model performance and borrower outcomes. We design and internally validate an explainable hybrid artificial-intelligence framework stratified by client tenure into two production models: a Weight-of-Evidence (WOE) logistic-regression scorecard for new salary-project applicants, and a hybrid scorecard for repeat applicants, in which a stacked ensemble of LightGBM, CatBoost and a multi-head self-attention neural network contributes a single WOE-encoded predictor to a second-stage L2-regularized logistic regression. The hybrid recovers a substantial share of the ensemble’s discriminatory lift while preserving an auditable, monotone scorecard at the point of decision, and isotonic recalibration restores the predicted probabilities of default to the empirical bad-rate scale required for IFRS 9 expected-credit-loss accrual and risk-based pricing. We report discrimination, calibration and stability evidence under a strict anti-leakage protocol and set out the structural preconditions under which the architecture transfers to other emerging-market payroll-anchored portfolios. We are explicit about scope: a true out-of-time validation and a full group-conditional fairness audit are identified as required next steps rather than claimed here. The contribution is a reproducible, interpretable scoring design that exploits payroll visibility while retaining full coefficient interpretability inside the production decision engine.
Gulnaz Zakariya, A. Moldagulova, Nor’ashikin Ali· Applied Informatics· 0 citations
Forecasting corporate earnings growth is fundamental to investment, credit, and regulatory decision-making. Existing forecasting approaches either rely on restrictive linear assumptions or provide limited interpretability, making them less suitable for high-stakes financial applications. This study proposes a transparent and causally informed framework for predicting future corporate earnings growth from financial statement data. We present TRuE-XAI (Transparent, Rule-based, and Explainable Artificial Intelligence), an integrated framework combining imbalance-aware ensemble learning, automated hyperparameter optimization, rule-based explainability, visual analytics, and causal inference. Random Forest, XGBoost, and LightGBM classifiers were optimized using Optuna and Hyperopt and evaluated with multiple class-balancing strategies, including SMOTE, ADASYN, TomekLinks, and Repeated Edited Nearest Neighbours (RENN). Experiments were conducted on real-world SEC-derived quarterly financial statement data from U.S. publicly listed firms covering 2014–2024. Model transparency was achieved through Anchor explanations, multi-metric feature-importance analysis, SilVA visual analytics, and causal effect estimation using DoWhy and EconML. The best-performing configuration, TomekLinks–XGBoost, achieved an F1-score of 0.467 and accuracy of 0.849 on the real SEC dataset while maintaining stable generalization under a leakage-free evaluation protocol. Anchor explanations generated concise, high-precision IF–THEN rules that explained individual predictions, whereas complementary feature-importance analyses identified consistent financial drivers across models. Causal inference showed that Net Profit Margin Change, Sales Growth, EBIT, and Asset Turnover exert positive causal effects on the probability of future earnings growth, while Inventory to Total Assets and Cash Flow to Net Income exhibited negative causal effects. Placebo and refutation tests supported the robustness of the estimated treatment effects. TRuE-XAI integrates predictive modelling, explainable AI, visual analytics, and causal inference into a unified framework for transparent earnings-growth forecasting. By combining competitive predictive performance with interpretable decision rules and causally grounded insights, the framework provides a practical and trustworthy approach for financial decision support and demonstrates how explainable and causal machine learning can be applied in regulated financial environments.
G. Jamnal· Frontiers in Artificial Inte...· 0 citations