Explainable Machine Learning for Credit Risk Management and Intelligent Lending Decisions in Nepalese Cooperative Banks: A Mathematical Review
TL;DR
It is argued that predictive accuracy and regulatory transparency are not competing objectives but complementary necessities for institutional survival in Nepal’s cooperative sector.
Abstract
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.