Abstract
This research explores the integration of Big Data technologies in risk management strategies within the banking sector through a systematic literature review. The study identifies the key methods and frameworks that enhance the ability of financial institutions to predict and mitigate risks more effectively. By leveraging predictive analytics, particularly with Machine Learning (ML) and Internet of Things (IoT) data, banks can anticipate potential risks with greater precision, improving decision-making speed and accuracy. The review highlights the significant benefits of Big Data, including reductions in financial losses, enhanced risk prediction accuracy, and improved operational efficiency. Case studies demonstrate how these technologies have contributed to more resilient and proactive risk management practices. However, challenges related to data privacy, cybersecurity, and infrastructure costs persist. This research provides insights into the transformative impact of Big Data on risk management in banking, while also suggesting directions for future research to overcome existing barriers and optimize integration. The findings underscore the importance of a strategic approach to Big Data implementation, which could lead to more robust risk management systems and greater financial stability in the banking sector.
Published Version
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