Implementation of Big Data Analytics in Credit Risk Management in the Banking and Financial Services Sector: A Contemporary Literature Review

Abstract

The study conducts a comprehensive contemporary literature review taking the existing works between 2016 to 2022 to investigate the utilization of Big Data analytics in the banking and financial services industry, with a specific focus on the application of these techniques in credit risk management and assessment. The findings indicate that IEEE has been the leading publisher in this field, and the most prevalent domain is business, management, and accounting. Additionally, it was observed that China has been the most significant contributor in this area of research. In addressing the research questions, it was determined that Random Forest and Random Forest-based techniques have been the most deployed for credit risk assessment such as loan default, and it has been demonstrated that integrated algorithms are efficient. However, the potential of other data processing algorithms with Random Forest in this domain requires further exploration. Furthermore, further research is necessary to address and overcome the challenges and obstacles encountered in addressing the third research question. This review provides a useful reference source in guiding the credit risk management and assessment using Big Data analytics for both academic and practical industries.

Keywords: Big Data Analytics, Finance, Banking and Financial Services Sector, Credit Risk Management, Machine Learning, Artificial Intelligence