RISK MANAGEMENT IN BANKING USING MACHINE LEARNING TECHNIQUES AS AN ALTERNATIVE ANALYSIS TOOL – A REVIEW

Technological applications are playing a more influential role in management in the contemporary business environment. Machine learning, artificial intelligence, and other algorithmic applications are some of the most common influencers in business applications. They present numerous solutions to business management problems, including banking risk management. In the last decade, risk management has gained greater prominence in financial services. In the past, banks focused on the detection, measuring, and reporting of risks. However, they are now leveraging on machine learning for greater accuracy and efficacy in risk management. As such, this paper explored different ways that machine learning can be applied in banking risk management. To achieve the objective of this study, the researcher conducted a comprehensive literature review on the topic of machine learning in banking risk management. The researcher found considerable industry and academic research focusing on developments in the financial services industry, especially in relation to risk management. It reviewed the literature, analyzing and evaluating various risk management machine-learning techniques. It identified risk management problem areas and explored various ways of addressing them. The review showed that machine learning in risk management in the financial services sector was still under-researched. While there were many studies on credit risks, other risks such as liquidity risks, market risks, and operational risks saw minimal attention. Nevertheless, machine learning applications were found to have the potential to develop more effective risk management models. Machine learning is leveraged on different data types to predict potential events with greater accuracy and estimate losses associated with different risk types. In addition, the machine learning techniques in risk management were found to provide better and more accurate results than traditional statistical models. Though machine learning suggests improving banking risk management, there are some areas that need further study. For instance, the paper suggested in-depth studies on machine learning models for different types of banking risks.