The rapid proliferation of financial technology has introduced a paradigm shift in credit risk assessment, particularly within emerging markets where traditional credit bureau data is often scarce or non-existent. While advanced Machine Learning (ML) algorithms—such as Gradient Boosting and Deep Neural Networks—demonstrate superior predictive accuracy compared to traditional statistical methods, their deployment is frequently hindered by their inherent opacity. This "black box" nature presents a significant barrier to adoption in regulated financial environments where explainability is a prerequisite for trust, fairness, and regulatory compliance. This paper addresses the critical dichotomy between predictive performance and model interpretability in the context of lending to the unbanked population. We propose a robust framework that integrates high-performance non-linear models with post-hoc explainability techniques, specifically Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). By utilizing a dataset proxying emerging market credit profiles, we demonstrate that it is possible to maintain the high accuracy of complex ensemble models while providing granular, human-understandable explanations for individual credit decisions. The findings suggest that the integration of Explainable AI (XAI) can unlock the potential of alternative data in emerging economies, fostering financial inclusion without compromising risk management standards.
Keywords: Credit Risk, Explainable AI (XAI), Emerging Markets, Financial Inclusion, Machine Learning, SHAP.