Predictive Intelligence: The Impact of Risk Modeling on Organizational Resilience
Prakash Somasundaram1, K Aishwarya Pillai2
1Prakash Somasundaram, Department of Computer Science, Northeastern University, San Francisco, California, United States of America (USA).
2K Aishwarya Pillai, Department of Computer Science, Northeastern University, San Francisco, California, United States of America (USA).
Manuscript received on 12 January 2024 | Revised Manuscript received on 16 January 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024 | PP: 1-3 | Volume-13 Issue-3, February 2024 | Retrieval Number: 100.1/ijitee.C980213030224 | DOI: 10.35940/ijitee.C9802.13030224
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The importance of risk modelling in strategic planning and decision-making cannot be emphasized enough in an age where uncertainty is constant. This paper explores the crucial function of risk modelling as a vital resource for people and organizations trying to understand and reduce potential risks. The emergence of big data and technology has made it possible to create complex risk models that offer more insights into possible hazards and improve the efficacy of risk management choices. In order to properly measure and manage risks, this article highlights the value of risk models across a range of industries. Risk models are essential for regulatory compliance, especially in sectors like healthcare, where following laws like GDPR and HIPAA are required. These models offer a consistent method for evaluating risk, guaranteeing adherence and averting costly fines. Additionally covered in the article is how risk models improve decision-making by lowering uncertainty and increasing openness, which in turn fosters stakeholder trust. The paper’s main body describes a thorough approach to risk modelling, which begins with identifying potential risks and progresses to risk assessment through the use of methods like fault tree analysis (FTA) and event tree analysis (ETA). It then goes over how to quantify risks using both quantitative and qualitative approaches, and it ends with adding up every potential threat to provide an overall risk profile for the company. This paper concludes by highlighting the need for risk modelling in the intricate corporate world. It offers a thorough process for creating risk models, matching them to organizational goals, and utilizing them as a preventative measure for resilience and long-term performance.
Keywords: Organizational Risk Management, Risk Modeling, Regulatory Compliance, Risk Assessment Techniques.
Scope of the Article: Simulation Optimization and Risk Management