The Value of AI and Predictive Analytics ERM Across Industries (Essay Sample)

The Value of AI and Predictive Analytics ERM Across Industries Name Institution Course Date The Value of AI and Predictive Analytics ERM Across Industries Risks are inherent across industries in today’s global economic scenario. Successful business operations rely on the management’s ability to manage the risks and minimize their negative impacts. The increasing adoption of complex digital ecosystems has made it more challenging for risk assessment and management experts. On the other hand, decision-making has become more accurate with the power of data and analytics. With data and analytics, organizations can unlock several opportunities for innovation. Artificial intelligence (AI) algorithms and Machine Learning (ML) technologies have emerged as powerful statistical analysis tools for predictive risk assessment across industries. These technologies can predict risk occurrence and impact accurately and efficiently. AI and ML leverage big data analysis to identify patterns and make predictions, enabling organizations to enhance their predictive analytical capabilities and risk management strategies ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"nkXffwfC","properties":{"formattedCitation":"(Kalogiannidis et al., 2024)","plainCitation":"(Kalogiannidis et al., 2024)","noteIndex":0},"citationItems":[{"id":477,"uris":["http://zotero.org/users/local/r6h8rKwY/items/NQQEWQYN"],"itemData":{"id":477,"type":"article-journal","abstract":"This study examined the efficacy of artificial intelligence (AI) technologies in predictive risk assessment and their contribution to ensuring business continuity. This research aimed to understand how different AI components, such as natural language processing (NLP), AI-powered data analytics, AI-driven predictive maintenance, and AI integration in incident response planning, enhance risk assessment and support business continuity in an environment where businesses face a myriad of risks, including natural disasters, cyberattacks, and economic fluctuations. A cross-sectional design and quantitative method were used to collect data for this study from a sample of 360 technology specialists. The results of this study show that AI technologies have a major impact on business continuity and predictive risk assessment. Notably, it was discovered that NLP improved the accuracy and speed of risk assessment procedures. The integration of AI into incident response plans was particularly effective, greatly decreasing company interruptions and improving recovery from unforeseen events. It is advised that businesses invest in AI skills, particularly in fields such as NLP for automated risk assessment, data analytics for prompt risk detection, predictive maintenance for operational effectiveness, and AI-enhanced incident response planning for crisis management.","container-title":"Risks","DOI":"10.3390/risks12020019","ISSN":"2227-9091","issue":"2","language":"en","license":"http://creativecommons.org/licenses/by/3.0/","note":"number: 2\npublisher: Multidisciplinary Digital Publishing Institute","page":"19","source":"www.mdpi.com","title":"The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece","title-short":"The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity","URL":"https://www.mdpi.com/2227-9091/12/2/19","volume":"12","author":[{"family":"Kalogiannidis","given":"Stavros"},{"family":"Kalfas","given":"Dimitrios"},{"family":"Papaevangelou","given":"Olympia"},{"family":"Giannarakis","given":"Grigoris"},{"family":"Chatzitheodoridis","given":"Fotios"}],"accessed":{"date-parts":[["2024",4,16]]},"issued":{"date-parts":[["2024",2]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Kalogiannidis et al., 2024). These technologies have a significant influence on anticipating operational risks, making it possible for organizations to adopt them in addressing potential risks and reducing their impact on normal business operations. AI and ML digital technologies use past and present data to perform predictive risk analytics, enabling organizations to respond appropriately. Organizations are also able to examine how their risk profile changes over time. ML and data mining tools can provide a large amount of data for accurate predictions. When an organization faces a catastrophic impact due to the occurrence of an event, predictive risk analytics enables the management to analyze the root cause and apply risk mitigation measures to ensure that the incident will not occur in the future, or if it occurs, the impact remains minimal. For predictive risk analytics to be useful for any organization, the organization must have a well-defined risk strategy to monitor and predict risks. Predictability is important for the effective management of risks because it allows organizations to anticipate and prepare for potential adverse incidents ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"tsATPDti","properties":{"formattedCitation":"(Mohammed & Knapkova, 2016)","plainCitation":"(Mohammed & Knapkova, 2016)","noteIndex":0},"citationItems":[{"id":479,"uris":["http://zotero.org/users/local/r6h8rKwY/items/TQHNKQCN"],"itemData":{"id":479,"type":"article-journal","abstract":"Traditionally risk management used to be considered as a means to alleviate perhaps eliminate negative outcomes of exposures. However, the result of this and other empirical studies shows the ability of risk management to go beyond this and respond to market factors which are out of management control in order to control volatilities in earning which ultimately improve corporate performance. The empirical study investigates the relationship between total risk management and company's performance. The result reviled that there is a positive relationship between total risk management and company's performance in companies which have invested higher level of intellectual capital. The result of the empirical study is consistent with other studies in different economic phenomenon.","container-title":"Procedia - Social and Behavioral Sciences","DOI":"10.1016/j.sbspro.2016.05.499","journalAbbreviation":"Procedia - Social and Behavioral Sciences","page":"271-277","source":"ResearchGate","title":"The Impact of Total Risk Management on Company's Performance","volume":"220","author":[{"family":"Mohammed","given":"Hamdu"},{"family":"Knapkova","given":"Adriana"}],"issued":{"date-parts":[["2016",5,1]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Mohammed & Knapkova, 2016). When risks become predictable, it means the management has a certain level of certainty in understanding what will happen in the future, and its impacts on business. This predictability enables risk managers to identify risks, assess their impact, plan mitigation strategies, and allocate resources more efficiently. Without predictability, organizations would be too scared to take risks or actions to mitigate potential risks, and their future uncertain. Effective management of risks helps build more resilient organizations. First, understanding the context of an organization and its objectives helps identify the sources and impacts of potential risks, and also opportunities and strengths that can be leveraged to realize growth. Second, identifying and assessing risks provides a comprehensive and diverse perspective for risk control and mitigation strategies. Responding to the risks means reducing or eliminating the source of the risk, and thus, its impact. Organizations can also enhance their res...