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This comprehensive review explores the dynamic landscape of predictive analytics in credit risk management within the banking sector. Anchored in a qualitative research design, the study synthesizes existing literature and real-world case studies to provide a multifaceted understanding of predictive analytics' role in modern banking. The review identifies key trends, highlighting the integration of predictive analytics across diverse banking operations, the transition to advanced machine learning algorithms, the democratization of predictive analytics tools, and the growing emphasis on ethical and regulatory compliance. It underscores the effectiveness of predictive analytics, showcasing its ability to enhance risk assessment precision, decision-making agility, and overall banking performance. Comparative analyses reveal the varying performance of predictive models across contexts, emphasizing the importance of tailored model selection. However, challenges such as data quality, model interpretability, talent scarcity, ethical considerations, and implementation costs pose significant hurdles. Looking forward, predictive analytics promises to be an indispensable tool for mitigating credit risk in the banking sector, offering refined risk assessments, smarter decisions, and enhanced resilience. The insights from this review provide valuable guidance for banking professionals, regulators, and researchers navigating the evolving landscape of predictive analytics in banking.
Addy et al. (Wed,) studied this question.