We extend a residual-learning hybrid credit scoring framework (logistic regression scorecard plus a gradient-boosting correction on its residuals, decomposed at each prediction into an interpretability ratio $\rho(x)$ that measures the share attributable to the linear branch) along three axes: an East African empirical instantiation on the Zindi Financial Inclusion in Africa data (Kenya, Rwanda, Tanzania, Uganda); a fairness audit at the granularity of the framework's three interpretability regions; and a thin-file segmentation analysis. On the Taiwan Credit Default benchmark retained for continuity, the calibrated hybrid attains AUC $= 0.776$ ($\Delta\mathrm{AUC} = +0.057$ vs.\ standalone logistic regression, $+0.001$ vs.\ standalone XGBoost), reduces Brier Score by 23\%, and concentrates the highest-default-rate borrowers (69.5\%) in the fully interpretable region. On Zindi, the calibrated hybrid attains AUC $= 0.869$ ($\Delta\mathrm{AUC} = +0.015$ vs.\ LR, $p<0.001$; $-0.004$ vs.\ XGBoost), cuts Brier from $0.158$ to $0.085$ (a 46\% reduction), and replicates the regional routing pattern. The fairness audit detects severe routing into the opaque ML-driven region along socioeconomic axes: rural respondents by 18 percentage points relative to urban, primary-or-less-educated by 32 points relative to secondary-and-above, and Ugandan respondents by 22 points relative to Kenyan, while gender shows essentially no routing disparity. The audit pipeline surfaces subgroup-routing violations that aggregate fairness metrics miss, in a form directly usable by African central-bank supervisors of digital credit.
Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients are easy to interpret, but it can miss nonlinear structure. Flexible models can improve prediction, but their explanations are often post-hoc and may not describe the decision rule itself. We introduce $\texttt{findr}$, short for flexible, interpretable deep regression, a semi-structured framework for binary credit risk modelling that decomposes the logit into an interpretable structured component and an orthogonal neural residual. The orthogonalisation separates coefficient-based effects from residual nonlinear variation, while an in-processing Wasserstein penalty mitigates group disparities by comparing score distributions during training. The framework also includes diagnostics that measure the structured component's contribution to logit variation, decision agreement, and local directional consistency. We evaluate $\texttt{findr}$ in a simulation study and on eight public credit datasets using score-level accuracy-fairness frontiers. The results show that $\texttt{findr}$ behaves close to logistic regression when the signal is approximately linear, while recovering much of the predictive gain of neural models when nonlinear structure is relevant. The diagnostics identify when coefficient-based explanations remain close to the full fitted model and when residual variation must also be examined. These findings support semi-structured modelling as a practical way to make performance, fairness, and interpretability trade-offs explicit in credit risk decisions.
Victor Medina-Olivares, Stefan Lessmann, Jonathan Crook· 0 citations
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
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
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
India's Unified Payments Interface (UPI) generates rich behavioral data for over 400 million active users, yet this transactional signal remains inaccessible to most lenders for credit assessment due to data privacy restrictions. Thin-file borrowers — individuals with limited formal credit history — represent the primary beneficiaries of UPI-based credit assessment and simultaneously the population for whom bureau-based models perform least reliably. This study proposes, empirically validates, and evaluates six UPI behavioral proxy features (F4) constructed from standard loan application variables, providing a publicly replicable framework for approximating UPI transaction signals in credit scoring. Scientific validation via Spearman correlation confirms that payment_discipline_score and upi_success_ratio_proxy exhibit the expected directional relationships with default in both independent datasets (p<0.001). Using DS1: LendingClub 2016-2018 (N=300,001) and DS2: Home Credit (N=307,511) with five machine learning models, results show F4 features consistently improve default detection Recall for thin-file borrowers across all models and both datasets. CatBoost achieves +9.95% Recall gain (DS1) and Logistic Regression +6.80% (DS2). SHAP attribution confirms F4 proxies account for 19.0%–32.2% of total predictive power for thin-file borrowers, with payment_discipline_score ranking as the single most predictive feature in Home Credit above all bureau variables. These findings establish UPI behavioral proxies as a meaningful, scientifically validated, and previously underquantified dimension of creditworthiness.
Deep Shikha, Himanshu Vasnani· Global Journal of Engineerin...· 0 citations
A novel Ranked Attribute Selection with Midpoint Filtering with Midpoint Filtering (RASF) framework that extends LCS (EXTRACS) to enhance feature selection and rule validation for credit approval and supports explainable AI in credit scoring.
M. Ahamed, Abubakar Siddique, Trung Nguyen et al.· Proceedings of the Genetic a...· 0 citations