This research paper explores the credit risk assessment practices in non-banking financial institutions (NBFIs) with a focus on lessons learned from the shadow banking sector. NBFIs have gained significant prominence in the financial landscape, and their role in credit intermediation has expanded. However, the inherent complexities and unique characteristics of NBFIs pose challenges to credit risk assessment. Drawing insights from the shadow banking sector, this study aims to identify key lessons and best practices that can enhance credit risk assessment in NBFIs. The research adopts a qualitative approach, analyzing relevant literature, regulatory frameworks, and case studies to develop a comprehensive understanding of credit risk assessment practices in NBFIs. The findings highlight the importance of robust risk management frameworks, adequate risk governance, effective monitoring mechanisms, and the use of innovative tools and technologies in mitigating credit risks in NBFIs. The research concludes by providing recommendations for policymakers, regulators, and NBFIs to strengthen credit risk assessment practices and ensure the stability and resilience of the financial system.
Shadow Banking Sector, Machine Learning
