A SYSTEMATIC REVIEW OF CREDIT RISK ASSESSMENT MODELS IN EMERGING ECONOMIES: A FOCUS ON BANGLADESH'S COMMERCIAL BANKING SECTOR

Abstract

This systematic literature review explores the evolution, application, and performance of credit risk assessment models in emerging economies, with a focused lens on Bangladesh’s commercial banking sector. In an environment marked by institutional constraints, limited data infrastructure, and evolving regulatory frameworks, selecting the appropriate credit risk model is critical for financial stability and inclusion. Drawing from a total of 98 peer-reviewed studies published up to 2022, this review synthesizes evidence from academic and applied research to evaluate traditional statistical models—such as logistic regression and discriminant analysis—as well as machine learning approaches including support vector machines, decision trees, and neural networks. The review follows the PRISMA 2020 guidelines to ensure transparency, replicability, and methodological rigor throughout the review process. Key findings indicate that while machine learning models consistently outperform traditional models in terms of predictive accuracy, they are rarely adopted at scale due to concerns about model interpretability, regulatory acceptance, and institutional readiness. Furthermore, the review identifies major gaps in sector-specific model development, integration of alternative and real-time data, and post-deployment performance monitoring. The synthesis reveals that most models are designed generically, with limited adaptation to specific industries such as garments, agriculture, SMEs, and microfinance, thereby reducing their predictive relevance in context. Additionally, institutional barriers including lack of analytical expertise, fragmented IT infrastructure, and vague regulatory guidelines hinder the operationalization of advanced credit risk tools. The findings emphasize the necessity of aligning model sophistication with contextual realities, and the importance of balancing predictive performance with explainability and institutional capacity. This review offers an evidence-based foundation for policymakers, banking professionals, and researchers seeking to develop more inclusive, accurate, and operationally viable credit risk models in emerging-market financial ecosystems.