An Empirical Investigation In Anlysing The Critical Factors Of Machine Learning Towards Risk Management In Banks Using Multivariate Analysis Of Variance (Manova)

Abstract

The management of modern enterprises is increasingly dependent on various forms of technological assistance. Applications of machine learning, artificial intelligence, and other types of algorithmic software have become some of the most pervasive impacts on commercial software. They provide a comprehensive range of services for the administration of businesses, one of which is support with the management of banking risks. The importance of risk management in the financial services industry, particularly banking and insurance, has expanded dramatically during the past decade. Throughout the course of their existence, financial institutions have traditionally placed a significant emphasis on risk monitoring, analysis, and reporting. But these days, in order to control risks in a more accurate and effective manner, they use machine learning. In light of this, the objective of this study was to conduct an investigation into the numerous applications of machine learning in banking risk management. In order to accomplish the objectives of the study, the researcher carried out a comprehensive literature review on the application of machine learning to banking risk management. According to the findings of the study, there is a plethora of academic and professional literature on the subject of transformations in the financial services industry, particularly as they pertain to risk management. It did a literature analysis on the subject, examined several different machine learning algorithms, and graded them according to their potential usefulness in risk management. It identified areas in which risk management may be enhanced and investigated potential solutions to the identified issues. The findings of the review indicate that there is a major information gap in the area of the potential role that machine learning could play in risk management within the financial services industry. Many research concentrated on credit risks, while liquidity, market, and operational risks were often disregarded in these investigations. On the other hand, it was discovered that applications of machine learning have the potential to be utilized to construct risk management models. Machine learning is applied to several data kinds for the purpose of conducting analysis in order to improve the accuracy with which likely occurrences are estimated and to compute losses associated with various sorts of risks. Furthermore, it was demonstrated that when it comes to the management of risks, techniques based on machine learning are superior to traditional statistical models in terms of their accuracy and precision. Even if machine learning makes it possible for banks to better manage risk, additional research is still required in a number of other areas.