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Machine Learning for Risk Analysis
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Author(s): Parita Jain (KIET Group of Institutions, India), Puneet Kumar Aggarwal (ABES Engineering College, India), Kshirja Makar (HMR Institute of Technology and Management, India), Riya Garg (HMR Institute of Technology and Management, India), Jaya Mehta (HMR Institute of Technology and Management, India)and Poorvi Chaudhary (HMR Institute of Technology and Management, India)
Copyright: 2022
Pages: 24
Source title:
Applications of Computational Science in Artificial Intelligence
Source Author(s)/Editor(s): Anand Nayyar (Duy Tan University, Da Nang, Vietnam), Sandeep Kumar (CHRIST University (Deemed), Bangalore, India)and Akshat Agrawal (Amity University, Guragon, India)
DOI: 10.4018/978-1-7998-9012-6.ch009
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Abstract
Evolution of technology summons risks. With the use of complex prototypes and methods, not only the decision-making propensity of the machines increases but also the risk assessment reduces and frauds increased. Machine learning (ML) is considered an appropriate solution for the management of risks as it can produce the desired solution with less human effort. So, to minimize the possibility of risks, certain methods are adopted that benefited through ML. The chapter provides an insight into various applications of ML techniques in the field of risk analysis. The application of ML in this sector has led to a fact that these methods can be used to analyze huge amounts of data with efficient predictive analysis. Moreover, the future of machine learning in risk analysis and management is presented bringing out the positive picture. As a conclusion, one can just say that humans will be seeing an era which will make even complex problems easy to solve with efficiency. The chapter concludes with some limitations which need to be fixed for better risk management.
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