The corporate downfalls of the early 2000s, the global financial crisis of the last years, and the recent outbreak of the COVID-19 pandemic have pushed companies into making efforts to improve Risk Management (RM) practices (PwC,2020). RM covers different applications and technological fields, involves both business and operational aspects, and affects all sectors at various levels and with different magnitudes. From an Information Technology (IT) perspective, RM can have dual value (Sanford & Moosa,2015): (1) operational RM and (2) data availability to applications, digital services, and lines of business. This last aspect is perhaps the most critical, especially today in the era of big data (BD), where application tasks are increasingly data intensive (Martínez-Rojas et al.,2018). For this reason, it is becoming increasingly important to invest in data to model cross-cutting RM solutions and strategies that find the right support in infrastructure (Dicuonzoet al.,2019; Sundhararajan et al.,2018).The increase in the quantity and quality of data stored or accessible by companies,the growing value of data, and the spread of national and international regulations require companies to manage data and information (Fenz & Neubauer,2018). On the one hand, from the perspective of RM, the concept of“data availability”translates into data accessibility and usability by IT systems or“data-intensive”tasks, such as those of BD analytics or artificial intelligence (AI) (Sanford & Moosa,2015). On the other hand, the concept of“integrity”should be understood as a guarantee that information does not undergo changes or deletions as a result of errors or voluntary actions, but also as a result of malfunctions or damage of technological systems(Müller et al.,2018). In this case, the combination with RM is even stronger because data protection results in the mitigation of risks associated with accessing or using data improperly (Guha,2018).Digital innovation has not only brought new technologies such as the Cloud, theInternet of Things (IoT), machine learning, or AI to the market but has also centralized the importance of data to business managers (Ivanov et al.,2018;Wamba et al.,2015).From an infrastructure point of view, storage unites the various individual aspects of a company’s digital transformation. It represents the technological layer through which to distribute the data, making it available according to the speed and performance ideal for each type of business. Storage-related planning could be a risk for companies riding the wave of BD or AI:“data-intensive”tasks require adequate infrastructural support, but its programming may be insufficient for the most advanced workloads, resulting in operational risk resulting from data availability.On the other hand, overestimated planning involves risks, from economic to system management (Müller et al.,2018).The intelligence of machine learning technologies helps you better manage the risks of data availability because it allows you to predict storage capacity and performance needs, and even model and upgrade hardware systems accordingly.In this context, RM becomes an element of value. The basic principle is the elimination of manual operations, which is now possible thanks to technologies that enable companies to collect and analyze data from any source, of any size and nature, and anywhere. The challenge for businesses is not in data storage but in data’s availability, accessibility, and usability in order to be evaluated and exploited at multiple levels, by multiple people, with increasingly advanced tools, services, and applications (Martínez-Rojas et al.,2018).In light of these considerations, this work aims to fuel the already existing, but still underdeveloped, debate on the implementation of AI and BD by companies as tools to support RM, offering a systemization of the state of the art and orienting academics toward this issue. The analysis, therefore, aims to investigate the advantages of adopting new technologies in RM systems, identifying the main applications and benefits that can come from the application of AI and BD. It is intended, therefore, to provide an integrated cognitive framework of what has been developed so far by the literature, in order to highlight the possible evolution of studies in the field.In particular, we want to answer the following research question:RQ:What are the main issues that animate the scientific debate on artificial intelligence and big data as a tool to support risk management?We develop a literature review over the period 2010–2020. Our main findings underline that AI applications and exploiting the information potential of the large120G. Dicuonzo et al. amount of data managed by companies are becoming increasingly popular (Vostrikov et al.,2019). AI is among the main tools to support the RM function(Chen et al.,2012), allowing more effective RM (Hirsch,2018) and ensuring maximum reactivity and flexibility to anticipate unexpected events (Amaye et al.,2016; Engelseth & Wang,2018).This chapter is structured as follows: Section 2 outlines the methodology used;Section 3 outlines the main findings emerging from the literature review. Finally,Section4 contains the conclusions of the work

Big Data and Artificial Intelligence to Support Risk Management: A Systematic Literature Review

Grazia Dicuonzo
;
Francesca Donofrio;Graziana Galeone
2021

Abstract

The corporate downfalls of the early 2000s, the global financial crisis of the last years, and the recent outbreak of the COVID-19 pandemic have pushed companies into making efforts to improve Risk Management (RM) practices (PwC,2020). RM covers different applications and technological fields, involves both business and operational aspects, and affects all sectors at various levels and with different magnitudes. From an Information Technology (IT) perspective, RM can have dual value (Sanford & Moosa,2015): (1) operational RM and (2) data availability to applications, digital services, and lines of business. This last aspect is perhaps the most critical, especially today in the era of big data (BD), where application tasks are increasingly data intensive (Martínez-Rojas et al.,2018). For this reason, it is becoming increasingly important to invest in data to model cross-cutting RM solutions and strategies that find the right support in infrastructure (Dicuonzoet al.,2019; Sundhararajan et al.,2018).The increase in the quantity and quality of data stored or accessible by companies,the growing value of data, and the spread of national and international regulations require companies to manage data and information (Fenz & Neubauer,2018). On the one hand, from the perspective of RM, the concept of“data availability”translates into data accessibility and usability by IT systems or“data-intensive”tasks, such as those of BD analytics or artificial intelligence (AI) (Sanford & Moosa,2015). On the other hand, the concept of“integrity”should be understood as a guarantee that information does not undergo changes or deletions as a result of errors or voluntary actions, but also as a result of malfunctions or damage of technological systems(Müller et al.,2018). In this case, the combination with RM is even stronger because data protection results in the mitigation of risks associated with accessing or using data improperly (Guha,2018).Digital innovation has not only brought new technologies such as the Cloud, theInternet of Things (IoT), machine learning, or AI to the market but has also centralized the importance of data to business managers (Ivanov et al.,2018;Wamba et al.,2015).From an infrastructure point of view, storage unites the various individual aspects of a company’s digital transformation. It represents the technological layer through which to distribute the data, making it available according to the speed and performance ideal for each type of business. Storage-related planning could be a risk for companies riding the wave of BD or AI:“data-intensive”tasks require adequate infrastructural support, but its programming may be insufficient for the most advanced workloads, resulting in operational risk resulting from data availability.On the other hand, overestimated planning involves risks, from economic to system management (Müller et al.,2018).The intelligence of machine learning technologies helps you better manage the risks of data availability because it allows you to predict storage capacity and performance needs, and even model and upgrade hardware systems accordingly.In this context, RM becomes an element of value. The basic principle is the elimination of manual operations, which is now possible thanks to technologies that enable companies to collect and analyze data from any source, of any size and nature, and anywhere. The challenge for businesses is not in data storage but in data’s availability, accessibility, and usability in order to be evaluated and exploited at multiple levels, by multiple people, with increasingly advanced tools, services, and applications (Martínez-Rojas et al.,2018).In light of these considerations, this work aims to fuel the already existing, but still underdeveloped, debate on the implementation of AI and BD by companies as tools to support RM, offering a systemization of the state of the art and orienting academics toward this issue. The analysis, therefore, aims to investigate the advantages of adopting new technologies in RM systems, identifying the main applications and benefits that can come from the application of AI and BD. It is intended, therefore, to provide an integrated cognitive framework of what has been developed so far by the literature, in order to highlight the possible evolution of studies in the field.In particular, we want to answer the following research question:RQ:What are the main issues that animate the scientific debate on artificial intelligence and big data as a tool to support risk management?We develop a literature review over the period 2010–2020. Our main findings underline that AI applications and exploiting the information potential of the large120G. Dicuonzo et al. amount of data managed by companies are becoming increasingly popular (Vostrikov et al.,2019). AI is among the main tools to support the RM function(Chen et al.,2012), allowing more effective RM (Hirsch,2018) and ensuring maximum reactivity and flexibility to anticipate unexpected events (Amaye et al.,2016; Engelseth & Wang,2018).This chapter is structured as follows: Section 2 outlines the methodology used;Section 3 outlines the main findings emerging from the literature review. Finally,Section4 contains the conclusions of the work
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/375010
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