BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

Abstract

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.