Machine Learning for Risk Analysis

With the evolution of technology come along risks. With complex prototypes and methods, not only the decision-making propensity of the machines increases but also the risk assessment reduces and frauds increase. This calls for a better and improved version of risk analysis in the industry. Machine learning is considered an appropriate solution for the management of risks as it can produce the desired solution with less human effort. Risk is the probability of occurrence of failure of the system, which is unwanted in any project as it reduces the profit as well productivity of the system. So, to minimize the possibility of risks, certain methods are adopted, which operate through machine learning. Risk analysis mainly includes hazard identification, risk communication, risk assessment, and risk management. The chapter provides an insight into various applications of machine learning techniques in the field of risk analysis, which includes liquidity risk, operational risk, market risk, and RegTech. The application of machine learning in this sector has led to the conclusion that these methods or techniques can be used to analyze huge amounts of data with efficient predictive analysis. Various use cases have also been discussed such as supervision of conduct and market abuse in trading, fraud detection, and credit risk. Moreover, the future of machine learning in risk analysis and management is presented as well bringing out a positive picture. The chapter concludes with some limitations, which need to be fixed for better risk management.