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Risk Management in the Digital Age: Leveraging AI for Predictive Analysis and Mitigation

Abstract

With the digital era, Artificial Intelligence (AI) plays an evolving role in the process of risk management-from mere support in decision-making processes to now playing an essential role in predictive analysis and risk mitigation. The paper will explain how AI, particularly machine learning and predictive analytics, can better real-time risk identification, market trend forecasting, and develop proactive risk management strategies. Through a quantitative research approach that combines surveys, case studies, and performance metrics from 2015 to 2023, the study delves into the impact of AI across various industries. Key findings indicate that models driven by AI outperform traditional methods in terms of risk prediction and strategy formulation, significantly improving organizational resilience and preparedness in the face of unforeseen disruptions. The study also touches on challenges, such as integrating AI into legacy risk management frameworks, ensuring ethical deployment, and maintaining data privacy. The paper concludes with recommendations for leveraging AI to enhance risk management practices and future research directions.

References

  1. Smith, J., & White, A. (2021). "Artificial Intelligence in Risk Management: An Overview of Current Practices." Journal of Risk Management, 35(2), 125-144.
  2. Kumar, N. (2024). Innovative Approaches of E-Learning in College Education: Global Experience. E-Learning Innovations Journal, 2(2), 36–51. https://doi.org/10.57125/ELIJ.2024.09.25.03
  3. Dorota Jelonek, Narendra Kumar and Ilona Paweloszek(2024): Artificial Intelligence Applications in Brand Management, S I L E S I A N U N I V E R S I T Y O F T E C H N O L O G Y P U B L I S H I N G H O U S E SCIENTIFIC PAPERS OF SILESIAN UNIVERSITY OF TECHNOLOGY, Serial No 202, pp 153-170, http://managementpapers.polsl.pl/; http://dx.doi.org/10.29119/1641-3466.2024.202.10
  4. Narendra Kumar (2024): Research on Theoretical Contributions and Literature-Related Tools for Big Data Analytics, Sustainable Innovations in Management in the Digital Transformation Era: Digital Management Sustainability, Pages 281 – 288, January 2024, DOI 10.4324/9781003450238-28
  5. Johnson, H., & Liu, Z. (2022). "Machine Learning for Real-Time Risk Identification." International Journal of Predictive Analysis, 18(1), 50-67.
  6. Harris, T., & Green, B. (2020). "Forecasting Market Trends Using AI: A Comparative Study." Market Trends Review, 45(4), 322-340.
  7. Williams, R., & Thompson, K. (2021). "Proactive Risk Mitigation through Predictive Analytics." Journal of Business Strategy, 29(3), 78-92.
  8. Evans, M., & Hall, D. (2023). "The Integration of AI in Traditional Risk Management Frameworks." Risk Management Journal, 12(2), 150-170.
  9. Martinez, L., & Roberts, C. (2020). "Ethical Considerations in AI-Driven Risk Management." Journal of Ethics in Technology, 6(1), 21-39.
  10. Lee, Y., & Kim, S. (2022). "Enhancing Organizational Resilience Through AI Insights." Business Resilience Review, 40(3), 211-230.
  11. Zhang, W., & Wang, J. (2021). "AI in Predictive Analytics: Improving Accuracy in Market Forecasting." Journal of Financial Technology, 27(2), 101-118.
  12. Chen, F., & Kumar, S. (2023). "AI Algorithms and Their Impact on Risk Identification." Technological Innovations in Risk Management, 14(4), 98-115..
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How to Cite

Shabana Faizal, (2025-01-07 18:17:30.723). Risk Management in the Digital Age: Leveraging AI for Predictive Analysis and Mitigation. Abhi International Journal of Artificial Intelligence Applications in Management, Volume lBpPJ5OrRh2dLgwHVb1c, Issue 1.