Explainable and Ensemble-Based Machine Learning Frameworks for Credit Risk Assessment: Integrating Deep Learning, Alternative Data, and Interpretability in Modern Financial Decision-Making

Abstract

Credit risk assessment has undergone a profound transformation over the past two decades, driven by the rapid advancement of machine learning, the increasing availability of alternative and behavioral data, and the growing regulatory demand for transparency and fairness in automated decision-making systems. Traditional credit scoring approaches, particularly logistic regression, have long been favored for their interpretability and regulatory acceptance, yet they often struggle to capture nonlinear patterns and complex borrower behaviors embedded in modern financial data. In contrast, advanced machine learning and deep learning models demonstrate superior predictive power but introduce significant challenges related to explainability, bias, and operational trust. This research develops a comprehensive conceptual and methodological synthesis of ensemble-based, deep learning, and explainable artificial intelligence (XAI) approaches to credit risk modeling, grounded strictly in recent and foundational academic literature. By integrating heterogeneous balancing strategies, profit-sensitive learning, behavioral and transactional data, and post-hoc interpretability techniques such as SHAP and LIME, the study articulates how predictive accuracy, fairness, and transparency can be jointly optimized. A descriptive methodological framework is proposed, emphasizing ensemble logistic regression, deep neural architectures, alternative data integration, and explainability layers. The findings suggest that hybrid and ensemble models, when augmented with robust XAI mechanisms, outperform single-model approaches not only in predictive reliability but also in regulatory compliance and stakeholder trust. The discussion critically examines trade-offs between profit maximization and equality, the ethical implications of alternative data usage, and the future role of explainable deep learning in credit risk management. This study contributes a unified theoretical perspective for financial institutions, regulators, and researchers seeking to balance innovation with accountability in credit decision systems.