Analyzing The Role of Analytics in Insurance Risk Management: A Systematic Review of Process Improvement and Business Agility


International Research Journal of Economics and Management Studies
© 2023 by IRJEMS
Volume 2  Issue 1
Year of Publication : 2023
Authors : Achuthananda Reddy Polu, Bhumeka Narra, Dheeraj Varun Kumar Reddy Buddula, Hari Hara Sudheer Patchipulusu, Navya Vattikonda, Anuj Kumar Gupta
irjems doi : 10.56472/25835238/IRJEMS-V2I1P142

Citation:

Achuthananda Reddy Polu, Bhumeka Narra, Dheeraj Varun Kumar Reddy Buddula, Hari Hara Sudheer Patchipulusu, Navya Vattikonda, Anuj Kumar Gupta. "Analyzing The Role of Analytics in Insurance Risk Management: A Systematic Review of Process Improvement and Business Agility" International Research Journal of Economics and Management Studies, Vol. 2, No. 1, pp. 325-332, 2023.

Abstract:

Companies rely on data-driven insights to improve decision-making and reduce uncertainty in the insurance industry, where risk management is an essential aspect of operations. This study delves into the revolutionary effects of analytics in risk management via the use of cutting-edge technologies like automation, AI, and predictive modelling. It examines various data sources, including internal, external, third-party, and sensor data, to assess their influence on risk identification, classification, and mitigation strategies. The study highlights the significance of emerging technologies such as robotic process automation (RPA), AI-powered chatbots, blockchain, and cloud computing in optimizing underwriting processes, claims management, and fraud detection. Additionally, it discusses how analytics-driven strategies contribute to business agility by enabling real-time decision-making, improving operational efficiency, and fostering adaptability in a rapidly evolving digital landscape. Furthermore, the paper addresses key challenges in implementing analytics, including data quality, integration, accessibility, and security concerns, which can impact the effectiveness of risk management frameworks. The study concludes by proposing future research directions focused on enhancing AI-driven risk assessment models, improving data governance, and exploring innovative approaches to regulatory compliance in risk management.

References:

[1] S. Liu, B. Zheng, T. Li, G. Li, and P. Shen, “Research on risk classification based on AHP in automobile insurance,” Proc. - 2016 Int. Conf. Smart Grid Electr. Autom. ICSGEA 2016, pp. 157–159, 2016, doi: 10.1109/ICSGEA.2016.18.
[2] V. N. Burkov, I. V. Burkova, K. E. Amelina, D. Y. Adamets, and I. V. Goroshko, “Management of Complex Project Risks Based on Qualitative Assessments,” Proc. 2018 11th Int. Conf. "Management Large-Scale Syst. Dev. MLSD 2018, pp. 1–3, 2018, doi: 10.1109/MLSD.2018.8551786.
[3] S. S. Weedige, H. Ouyang, Y. Gao, and Y. Liu, “Decision making in personal insurance: Impact of insurance literacy,” Sustain., vol. 11, no. 23, pp. 1–24, 2019, doi: 10.3390/su11236795.
[4] B. M.Sc.M.Tech.Ph.D, Ilanthirayan, and Gis, “Used Predictive Analytics for Customer Production In Insurance,” p. 10, 2019, doi: 10.15515/iaast.0976-4828.10.1.8994.
[5] J. Hayes, “Approaches to Risk Management in Research and Development: An Analysis of Public / Private Partnerships in Ireland,” p. 130, 2016.
[6] Q. Liu, “Research on Risk Management of Big Data and Machine Learning Insurance Based on Internet Finance,” J. Phys. Conf. Ser., vol. 1345, no. 5, 2019, doi: 10.1088/1742-6596/1345/5/052076.
[7] J. L. Hung, B. E. Shelton, J. Yang, and X. Du, “Improving Predictive Modeling for At-Risk Student Identification: A Multistage Approach,” IEEE Trans. Learn. Technol., 2019, doi: 10.1109/TLT.2019.2911072.
[8] J. Lathrop and B. Ezell, “A systems approach to risk analysis validation for risk management,” Saf. Sci., 2017, doi: 10.1016/j.ssci.2017.04.006.
[9] T. M. Kreuzer, M. Wilde, B. Terhorst, and B. Damm, “A landslide inventory system as a base for automated process and risk analyses,” Earth Sci. Informatics, 2017, doi: 10.1007/s12145-017-0307-5.
[10] P. Gutiérrez González and L. F. Andersson, “Managing financial constraints: undercapitalization and underwriting capacity in Spanish fire insurance,” Econ. Hist. Rev., 2018, doi: 10.1111/ehr.12529.
[11] M. A. Alba C, M. Daya, and C. Franck, “Tart Cherries and health: Current knowledge and need for a better understanding of the fate of phytochemicals in the human gastrointestinal tract,” Critical Reviews in Food Science and Nutrition. 2019. doi: 10.1080/10408398.2017.1384918.
[12] D. Good, “Predicting real-time adaptive performance in a dynamic decision-making context,” J. Manag. Organ., vol. 20, no. 6, pp. 715–732, 2014, doi: 10.1017/jmo.2014.54.
[13] O. Pasichnyi, F. Levihn, H. Shahrokni, J. Wallin, and O. Kordas, “Data-driven strategic planning of building energy retrofitting: The case of Stockholm,” J. Clean. Prod., 2019, doi: 10.1016/j.jclepro.2019.05.373.
[14] D. W. Kwak, Y. J. Seo, and R. Mason, “Investigating the relationship between supply chain innovation, risk management capabilities and competitive advantage in global supply chains,” Int. J. Oper. Prod. Manag., 2018, doi: 10.1108/IJOPM-06-2015-0390.
[15] A. Laha, Advances in Analytics and Applications. 2019. doi: 10.1007/978-981-13-1208-3.
[16] K. Lyubov, “Technological innovations in the insurance industry,” Rozpr. Ubezpieczeniowe, vol. 26, 2018.
[17] E. Kouzari, V. C. Gerogiannis, I. Stamelos, and G. Kakarontzas, “Critical success factors and barriers for lightweight software process improvement in agile development: A literature review,” in ICSOFT-EA 2015 - 10th International Conference on Software Engineering and Applications, Proceedings; Part of 10th International Joint Conference on Software Technologies, ICSOFT 2015, 2015. doi: 10.5220/0005555401510159.
[18] S. Gupta and P. Tripathi, “An emerging trend of big data analytics with health insurance in India,” in 2016 1st International Conference on Innovation and Challenges in Cyber Security, ICICCS 2016, 2016. doi: 10.1109/ICICCS.2016.7542360.
[19] K. Ramdass, “Key leadership factors towards business process improvement: A managerial focus,” in Portland International Conference on Management of Engineering and Technology, 2015. doi: 10.1109/PICMET.2015.7273104.
[20] Routhu, K., Bodepudi, V., Jha, K. M., & Chinta, P. C. R. (2020). A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems. Available at SSRN 5102662.
[21] Chinta, P. C. R., & Katnapally, N. (2021). Neural Network-Based Risk Assessment for Cybersecurity in Big Data-Oriented ERP Infrastructures. Neural Network-Based Risk Assessment for Cybersecurity in Big Data-Oriented ERP Infrastructures.
[22] Katnapally, N., Chinta, P. C. R., Routhu, K. K., Velaga, V., Bodepudi, V., & Karaka, L. M. (2021). Leveraging Big Data Analytics and Machine Learning Techniques for Sentiment Analysis of Amazon Product Reviews in Business Insights. American Journal of Computing and Engineering, 4(2), 35-51.
[23] Karaka, L. M. (2021). Optimising Product Enhancements Strategic Approaches to Managing Complexity. Available at SSRN 5147875.
[24] Chinta, P. C. R., & Karaka, L. M. Agentic Ai And Reinforcement Learning: Towards More Autonomous And Adaptive Ai Systems.
[25] Boppana, S. B., Moore, C. S., Bodepudi, V., Jha, K. M., Maka, S. R., & Sadaram, G. AI and ML Applications In Big Data Analytics: Transforming ERP Security Models For Modern Enterprises.
[26] Chinta, P. C. R., Katnapally, N., Ja, K., Bodepudi, V., Babu, S., & Boppana, M. S. (2022). Exploring the role of neural networks in big data-driven ERP systems for proactive cybersecurity management. Kurdish Studies.
[27] Chinta, P. C. R. (2022). Enhancing Supply Chain Efficiency and Performance through ERP Optimisation Strategies. Journal of Artificial Intelligence & Cloud Computing, 1(4), 10-47363.
[28] Sadaram, G., Sakuru, M., Karaka, L. M., Reddy, M. S., Bodepudi, V., Boppana, S. B., & Maka, S. R. (2022). Internet of Things (IoT) Cybersecurity Enhancement through Artificial Intelligence: A Study on Intrusion Detection Systems. Universal Library of Engineering Technology, (2022).
[29] Krutthika H. K. & A.R. Aswatha. (2021). Implementation and analysis of congestion prevention and fault tolerance in network on chip. Journal of Tianjin University Science and Technology, 54(11), 213–231. https://doi.org/10.5281/zenodo.5746712
[30] Krutthika H. K. & A.R. Aswatha. (2020). FPGA-based design and architecture of network-on-chip router for efficient data propagation. IIOAB Journal, 11(S2), 7–25.
[31] Krutthika H. K. & A.R. Aswatha (2020). Design of efficient FSM-based 3D network-on-chip architecture. International Journal of Engineering Trends and Technology, 68(10), 67–73. https://doi.org/10.14445/22315381/IJETT-V68I10P212
[32] Krutthika H. K. & Rajashekhara R. (2019). Network-on-chip: A survey on router design and algorithms. International Journal of Recent Technology and Engineering, 7(6), 1687–1691. https://doi.org/10.35940/ijrte.F2131.037619
[33] S. Ajay, et al., & Krutthika H. K. (2018). Source hotspot management in a mesh network-on-chip. 22nd International Symposium on VLSI Design and Test (VDAT-2018). https://doi.org/10.1007/978-981-13-5950-7_51

Keywords:

Insurance Risk Management, Business Agility, Process Improvement, Strategic Implications, Digital Transformation.