Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making

Irina V.; Denis A.

doi:https://doi.org/10.54216/AJBOR.000203

Full Length Article

Volume 0 • Issue 2 • PP: 83-96 • 2019

Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making

Irina V. Pustokhina ^1*

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,

Denis A. Pustokhin ²

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¹Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow 117997, Russia

²Department of Logistics, State University of Management , Moscow 109542, Russia

* Corresponding Author.

DOI https://doi.org/10.54216/AJBOR.000203

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Abstract

The impact of climate change has made responsible risk management a major research topic during the past 20 years. In conjunction with societal problems that affect the economies and cultures in which they function, industrial risks can release dangerous pollutants into the natural world. Advances in information and communication technology, particularly big data analytics, can contribute to the creation of fresh perspectives that enable the detection of business risks whose operations are unstable and the implementation of remedial actions. Although risk management has been the subject of numerous research, there are few that examine the impact of BDA. This study strives to offer a big data analytic framework that integrates a pipeline of statistical testing, data visualization, and machine learning algorithms to interpret market information. The applicability of our framework in recognizing and managing risks is demonstrated through a case study of the global commodity market. Extensive proof-of-concept experimentations validated the efficiency and effectiveness of the argued framework by providing useful insights about market behavior, which can lead the decision-making process to get informed risk management.

Keywords

[1]&nbsp &nbsp &nbsp Dicuonzo G. Galeone G. Zappimbulso E. &amp Dell'Atti V. (2019). Risk management 4.0: The role of big data analytics in the bank sector.&nbsp International Journal of Economics and Financial Issues &nbsp 9(6) 40-47. [2]&nbsp &nbsp &nbsp Choi T. M. Chan H. K. &amp Yue X. (2016). Recent development in big data analytics for business operations and risk management.&nbsp IEEE transactions on cybernetics &nbsp 47(1) 81-92. [3]&nbsp &nbsp &nbsp Popovič A. Hackney R. Tassabehji R. &amp Castelli M. (2018). The impact of big data analytics on firms&rsquo high value business performance.&nbsp Information Systems Frontiers &nbsp 20 209-222. [4]&nbsp &nbsp &nbsp Awwad M. Kulkarni P. Bapna R. &amp Marathe A. (2018 September). Big data analytics in supply chain: a literature review. In&nbsp Proceedings of the international conference on industrial engineering and operations management&nbsp (Vol. 2018 pp. 418-25). [5]&nbsp &nbsp &nbsp Malik P. (2013). Governing big data: principles and practices.&nbsp IBM Journal of Research and Development &nbsp 57(3/4) 1-1. [6]&nbsp &nbsp &nbsp Baaziz A. &amp Quoniam L. (2014). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry.&nbsp arXiv preprint arXiv:1412.0755. [7]&nbsp &nbsp &nbsp Ferraris A. Mazzoleni A. Devalle A. &amp Couturier J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance.&nbsp Management Decision &nbsp 57(8) 1923-1936. [8]&nbsp &nbsp &nbsp Baaziz A. &amp Quoniam L. (2015). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry.&nbsp Baaziz A. &amp Quoniam L.(2013). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry. International Journal of Innovation-IJI &nbsp 1(1) 19-25. [9]&nbsp &nbsp &nbsp Arunachalam D. Kumar N. &amp Kawalek J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues challenges and implications for practice.&nbsp Transportation Research Part E: Logistics and Transportation Review &nbsp 114 416-436. [10] Wang J. Zhang W. Shi Y. Duan S. &amp Liu J. (2018). Industrial big data analytics: challenges methodologies and applications.&nbsp arXiv preprint arXiv:1807.01016. [11] Wamba S. F. Gunasekaran A. Akter S. Ren S. J. F. Dubey R. &amp Childe S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research 70 356-365. [12] Ventola C. L. (2018). Big data and pharmacovigilance: data mining for adverse drug events and interactions. Pharmacy and therapeutics 43(6) 340. [13] LaValle S. Lesser E. Shockley R. Hopkins M. S. &amp Kruschwitz N. (2010). Big data analytics and the path from insights to value. MIT sloan management review. [14] Cerchiello P. &amp Giudici P. (2016). Big data analysis for financial risk management. Journal of Big Data 3(1) 1-12. [15] Goel P. Datta A. &amp Mannan M. S. (2017 December). Application of big data analytics in process safety and risk management. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 1143-1152). IEEE. [16] Wang G. Gunasekaran A. Ngai E. W. &amp Papadopoulos T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International journal of production economics 176 98-110.

References

[1] Dicuonzo, G., Galeone, G., Zappimbulso, E., & Dell'Atti, V. (2019). Risk management 4.0: The role of big data analytics in the bank sector. International Journal of Economics and Financial Issues, 9(6), 40-47.

[2] Choi, T. M., Chan, H. K., & Yue, X. (2016). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.

[3] Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20, 209-222.

[4] Awwad, M., Kulkarni, P., Bapna, R., & Marathe, A. (2018, September). Big data analytics in supply chain: a literature review. In Proceedings of the international conference on industrial engineering and operations management (Vol. 2018, pp. 418-25).

[5] Malik, P. (2013). Governing big data: principles and practices. IBM Journal of Research and Development, 57(3/4), 1-1.

[6] Baaziz, A., & Quoniam, L. (2014). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry. arXiv preprint arXiv:1412.0755.

[7] Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923-1936.

[8] Baaziz, A., & Quoniam, L. (2015). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry. Baaziz, A., & Quoniam, L.(2013). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry. International Journal of Innovation-IJI, 1(1), 19-25.

[9] Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436.

[10] Wang, J., Zhang, W., Shi, Y., Duan, S., & Liu, J. (2018). Industrial big data analytics: challenges, methodologies, and applications. arXiv preprint arXiv:1807.01016.

[11] Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.

[12] Ventola, C. L. (2018). Big data and pharmacovigilance: data mining for adverse drug events and interactions. Pharmacy and therapeutics, 43(6), 340.

[13] LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010). Big data, analytics and the path from insights to value. MIT sloan management review.

[14] Cerchiello, P., & Giudici, P. (2016). Big data analysis for financial risk management. Journal of Big Data, 3(1), 1-12.

[15] Goel, P., Datta, A., & Mannan, M. S. (2017, December). Application of big data analytics in process safety and risk management. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 1143-1152). IEEE.

[16] Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International journal of production economics, 176, 98-110.

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Pustokhina, Irina V., Pustokhin, Denis A.. "Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making." American Journal of Business and Operations Research, vol. Volume 0, no. Issue 2, 2019, pp. 83-96. DOI: https://doi.org/10.54216/AJBOR.000203

Pustokhina, I., Pustokhin, D. (2019). Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making. American Journal of Business and Operations Research, Volume 0(Issue 2), 83-96. DOI: https://doi.org/10.54216/AJBOR.000203

Pustokhina, Irina V., Pustokhin, Denis A.. "Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making." American Journal of Business and Operations Research Volume 0, no. Issue 2 (2019): 83-96. DOI: https://doi.org/10.54216/AJBOR.000203

Pustokhina, I., Pustokhin, D. (2019) 'Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making', American Journal of Business and Operations Research, Volume 0(Issue 2), pp. 83-96. DOI: https://doi.org/10.54216/AJBOR.000203

Pustokhina I, Pustokhin D. Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making. American Journal of Business and Operations Research. 2019;Volume 0(Issue 2):83-96. DOI: https://doi.org/10.54216/AJBOR.000203

I. Pustokhina, D. Pustokhin, "Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making," American Journal of Business and Operations Research, vol. Volume 0, no. Issue 2, pp. 83-96, 2019. DOI: https://doi.org/10.54216/AJBOR.000203

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