Machine Learning for Risk Analysis

Introduction

Machine Learning (ML) is the new utterance of technology. It has the proficiency of replacing emphatic programming of the devices. It is generally hinged on the conception that a substantial amount of data is provided and on the basis of that data and some algorithms the machine is trained, and different machine modules are fabricated. The decisions made by the machines based on data provided are immensely efficient and accurate. With the commencement of the contemporary aeon of technology, ingenious crimes and risks are also escalating. In the modern world the most important constituent to take off is the risk management and its intensifying production. This chapter comprises all the information regarding how machine learning has palliated in risk assessment (Aggarwal et al., 2021; Apostolakis,2004; Aven,2012).

Large scale organizations, companies, and institutions are prone to risks like frauds consequently they are sticking to various machine learning techniques that can prevent or abate these frauds or risks. The chapter is an amalgamation of various strategies for changing the perspective of risk assessment. On the basis of risk assessment with machine learning various case studies sheathing different aspects has been covered in the chapter. These case studies have effectively engulfed the seriousness of risk assessment. Case studies are basically an inspiration of the future consequently elucidating constructing techniques to diminish risks. Several applications are also incorporated in this chapter as well that lead us to the significance of risk assessment in several industries and organizations for protection from copious frauds and deceptions altogether with the integration of machine learning in this peculiar field (Chen et al., 2008; Cheng et al., 2016).

The resonance of this chapter is related to the arguments that can be a sense of salvation from risks that can emerge abundant provenances followed by a conclusion which covers the mantle of machine learning in the field of risk management as a whole.

Machine Learning

Machine learning can be described as a subcategory of the Artificial Intelligence (AI) field which has its main focus on examining and recognizing patterns and arrangements in data to facilitate features such as training, thinking, decision making, learning, and researching without interference. Machine learning allows the user to pack an enormous sum of data with a computer algorithm, allows the computer to examine and analyze the data to make recommendations based on the input received. If some features require redesigning, they are classified and improved for a better design for the future (Creedy, 2011; Alzubi et al., 2018; Comfort, 2019; Jain et al., 2020; Aggarwal et al., 2021; Singh et al., 2021).

The main aim of the technology is to produce an easy to use a mechanism which works on making decisions by the computational algorithm. Variables, algorithms, and innovations are accountable for making decisions. Awareness towards the solution is needed for the better learning of the systems working and thus helps in understanding the path to reach the result.

Figure 1.

Machine learning algorithm process

In the initial stages of implementation of the algorithm, input or data is provided to the machine provided that the result is already known for that set of feed data. The changes are then made to produce the result (Diekmann, 1992; Durga et al., 2009; Goodfellow et al., 2016). The efficiency of the result depends on the amount and the quantity of the data that is fed to the machine as shown in Figure 1.