Mejora de procesos en la gestión de riesgos mediante la integración de tecnologías avanzadas
DOI:
https://doi.org/10.55873/rad.v4i1.369Palabras clave:
adopción, capacitación, infraestructura, mitigación, predicciónResumen
La gestión de riesgos en los procesos ha experimentado un avance significativo con la integración de tecnologías avanzadas, como la inteligencia artificial (IA) y el aprendizaje automático (ML), que permiten una mayor precisión y eficiencia en la toma de decisiones. Este artículo revisa el impacto de estas tecnologías en la mejora de la gestión de riesgos, analizando diversos estudios sobre su implementación en sectores como la salud, la energía, la logística y la financiación. Se explora cómo el uso de estas herramientas ha optimizado la evaluación y mitigación de riesgos, mejorando la capacidad de anticipación y reduciendo la exposición a eventos adversos. Sin embargo, también se identifican desafíos en su adopción, como la calidad de los datos, la integración con sistemas existentes y la necesidad de personal capacitado. Los resultados indican que, aunque las tecnologías avanzadas tienen un gran potencial para mejorar los procesos de gestión de riesgos, su implementación efectiva requiere de una infraestructura adecuada y una formación técnica especializada. Este estudio contribuye al entendimiento de los beneficios y limitaciones de estas tecnologías en la mejora de la gestión de riesgos.
Descargas
Citas
Abdulla, Y. Y., & Al-Alawi, A. I. (2024). Advances in Machine Learning for Financial Risk Management: A Systematic Literature Review. 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 531-535. https://doi.org/10.1109/ICETSIS61505.2024.10459536
Chenya, L., Aminudin, E., Mohd, S., & Yap, L. S. (2022). Intelligent Risk Management in Construction Projects: Systematic Literature Review. IEEE Access, 10, 72936-72954. https://doi.org/10.1109/ACCESS.2022.3189157
Dixit, Avani, Chauhan, R., & Sha, R. (2024). Application of Smart Systems and Emerging Technologies for Disaster Risk Reduction and Management in Nepal. International Journal of Disaster Resilience in the Built Environment. https://doi.org/10.1108/IJDRBE-07-2023-0085/FULL/XML
García-García, S., García-Galindo, M., Arrese, I., Sarabia, R., & Cepeda, S. (2022). Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review. Medicina, 58(12), 1746. https://doi.org/10.3390/medicina58121746
Ionescu, S.-A., & Diaconita, V. (2023). Transforming Financial Decision-Making: The Interplay of AI, Cloud Computing and Advanced Data Management Technologies. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 18(6). https://doi.org/10.15837/ijccc.2023.6.5735
John, L. D., & McCallum, J. F. (2020). Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities. Therapeutic Innovation and Regulatory Science, 54(4), 88–99. https://doi.org/10.1007/S43441-019-00023-3/TABLES/2
Jomthanachai, S., Wong, W.-P., & Lim, C.-P. (2021). An Application of Data Envelopment Analysis and Machine Learning Approach to Risk Management. IEEE Access, 9, 85978-85994. https://doi.org/10.1109/ACCESS.2021.3087623
Menanno, M., Riccio, C., Benedetto, V., Gissi, F., Savino, M. M., & Troiano, L. (2024). An Ergonomic Risk Assessment System Based on 3D Human Pose Estimation and Collaborative Robot. Applied Sciences, 14(11), 4823. https://doi.org/10.3390/app14114823
Munawar, H. S., Mojtahedi, M., Hammad, A. W. A., Kouzani, A., & Mahmud, M. A. P. (2022). Disruptive technologies as a solution for disaster risk management: A review. Science of The Total Environment, 806, 151351. https://doi.org/10.1016/j.scitotenv.2021.151351
Noudeng, V., Pheakdey, D. V., Minh, T. T. N., & Xuan, T. D. (2024). Municipal Solid Waste Management in Laos: Comparative Analysis of Environmental Impact, Practices, and Technologies with ASEAN Regions and Japan. Environments, 11(8), 170. https://doi.org/10.3390/environments11080170
Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475-486. https://doi.org/10.1016/j.ssci.2019.06.001
Rahman, A., & Fang, C. (2019). Appraisal of gaps and challenges in Sendai Framework for Disaster Risk Reduction priority 1 through the lens of science, technology and innovation. Progress in Disaster Science, 1, 100006. https://doi.org/10.1016/j.pdisas.2019.100006
Rahmani, A. M., Rezazadeh, B., Haghparast, M., Chang, W.-C., & Ting, S. G. (2023). Applications of Artificial Intelligence in the Economy, Including Applications in Stock Trading, Market Analysis, and Risk Management. IEEE Access, 11, 80769-80793. https://doi.org/10.1109/ACCESS.2023.3300036
Rocchi, A., Chiozzi, A., Nale, M., Nikolic, Z., Riguzzi, F., Mantovan, L., Gilli, A., & Benvenuti, E. (2022). A Machine Learning Framework for Multi-Hazard Risk Assessment at the Regional Scale in Earthquake and Flood-Prone Areas. Applied Sciences, 12(2), 583. https://doi.org/10.3390/app12020583
Salamai, A., Hussain, O. K., Saberi, M., Chang, E., & Hussain, F. K. (2019). Highlighting the Importance of Considering the Impacts of Both External and Internal Risk Factors on Operational Parameters to Improve Supply Chain Risk Management. IEEE Access, 7, 49297-49315. https://doi.org/10.1109/ACCESS.2019.2902191
Shaheen, B. W., & Németh, I. (2022). Integration of Maintenance Management System Functions with Industry 4.0 Technologies and Features—A Review. Processes, 10(11), 2173. https://doi.org/10.3390/pr10112173
Shetty, P. (2024). AI and Security, From an Information Security and Risk Manager Standpoint. IEEE Access, 12, 77468-77474. https://doi.org/10.1109/ACCESS.2024.3408144
Singh, A., Paruthy, S. B., Belsariya, V., Chandra J, N., Singh, S. K., Manivasagam, S. S., Choudhary, S., Kumar, M. A., Khera, D., & Kuraria, V. (2023). Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence. Cureus. https://doi.org/10.7759/cureus.50203
Stettinger, G., Weissensteiner, P., & Khastgir, S. (2024). Trustworthiness Assurance Assessment for High-Risk AI-Based Systems. IEEE Access, 12, 22718-22745. https://doi.org/10.1109/ACCESS.2024.3364387
Varela-Vaca, A. J., Parody, L., Gasca, R. M., & Gomez-Lopez, M. T. (2019). Automatic Verification and Diagnosis of Security Risk Assessments in Business Process Models. IEEE Access, 7, 26448-26465. https://doi.org/10.1109/ACCESS.2019.2901408
Yang, M., Chen, P., & Liu, S. (2023). Graph neural networks for customer segmentation: Capturing complex relationships. Knowledge-Based Systems, 278, 110-125.
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2025 Zamora-Pastor, Antonio, Brisa Gabriela Llanos-Atachahua, Nikolai Lance Cauper-Acuña, Yngue Elizabeth Ramírez-Pezo
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
