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.
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).
Htet Nge Nge Ko, Aung Htoo Khine, Shadab Kalhoro et al.· Journal of Risk and Financia...· 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 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.
Zekun Li· Advances in Economics, Manag...· 0 citations
This work proposes a tenure-stratified hybrid framework that couples an online weight-of-evidence logistic regression (WOE-LR) scorecard with an offline self-attention stacked ensemble whose calibrated PD is quantile-binned, WOE-encoded, and re-injected into the online scorecard as a single auditable predictor.
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