AI-Driven Cloud Framework for Software Maintenance in Life Insurance Systems: Gray Relational Analysis of Risk, Security, and Scalability in SAP and Oracle EBS Deployments
DOI:
https://doi.org/10.15662/IJARCST.2023.0606016Keywords:
AI-driven Cloud Computing, Software Maintenance, Life Insurance Systems, Gray Relational Analysis (GRA), Risk Assessment, Security and Scalability, SAP, Oracle E-Business Suite (EBS), Predictive Maintenance, Large-Scale Deployment, Enterprise SystemsAbstract
The increasing digital transformation in the life insurance industry demands reliable, scalable, and secure software maintenance frameworks capable of managing complex enterprise environments. This research presents an AI-driven cloud framework designed to optimize software maintenance and operational resilience across SAP and Oracle E-Business Suite (EBS) ecosystems. The framework integrates artificial intelligence (AI) and gray relational analysis (GRA) to quantitatively evaluate relationships among key performance indicators (KPIs) related to risk, scalability, and security in large-scale deployments.Through intelligent automation and predictive analytics, the proposed system enhances fault detection, reduces downtime, and strengthens data integrity within life insurance applications hosted on cloud environments. The AI modules leverage supervised and unsupervised learning algorithms to anticipate maintenance requirements and classify system anomalies, while GRA facilitates multi-criteria decision-making to prioritize maintenance actions based on correlation levels.
Experimental evaluations demonstrate significant improvements in system reliability, cost efficiency, and compliance adherence, validating the framework’s capacity to handle high-volume data transactions securely. This study contributes to advancing software engineering practices by bridging AI-driven cloud automation with risk-informed maintenance strategies, thereby promoting sustainable digital transformation in life insurance organizations operating on SAP and Oracle EBS infrastructures.
References
1. Sivaraju, P. S. (2023). Global Network Migrations & IPv4 Externalization: Balancing Scalability, Security, and Risk in Large-Scale Deployments. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS (ISCSITR-IJCA), 4(1), 7-34.
2. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal ofComputer Science Applications and Information Technology, 5(1), 1-8.
3. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
4. Sugumar, R. (2022). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. IEEE 2 (2):1-6.
5. Girdhar, P., Virmani, D., & Saravana Kumar, S. (2019). A hybrid fuzzy framework for face detection and recognition using behavioral traits. Journal of Statistics and Management Systems, 22(2), 271-287.
6. Thambireddy, S., Bussu, V. R. R., & Joyce, S. (2023). Strategic Frameworks for Migrating Sap S/4HANA To Azure: Addressing Hostname Constraints, Infrastructure Diversity, And Deployment Scenarios Across Hybrid and Multi-Architecture Landscapes. Journal ID, 9471, 1297. https://www.researchgate.net/publication/396446597_Strategic_Frameworks_for_Migrating_Sap_S4HANA_To_Azure_Addressing_Hostname_Constraints_Infrastructure_Diversity_And_Deployment_Scenarios_Across_Hybrid_and_Multi-Architecture_Landscapes
7. Armbrust, M., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
8. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
9. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
10. Mittelstadt, B. D., et al. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21.
11. Kroll, J. A., et al. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–705.
12. Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23–45.
13. Sarker, I. H., Kayes, A. S. M., & Badsha, S. (2020). A survey of machine learning models for cloud security and compliance. ACM Computing Surveys, 53(1), 1–36.
14. IEEE. (2019). Ethically Aligned Design: Prioritizing Human Well-being with Autonomous and Intelligent Systems. IEEE.
15. European Commission. (2020). Ethics Guidelines for Trustworthy AI. High-Level Expert Group on Artificial Intelligence.
16. ISO/IEC. (2018). ISO/IEC 27001:2018 – Information Security Management Systems. ISO.
17. Pasumarthi, A. (2022). Architecting Resilient SAP Hana Systems: A Framework for Implementation, Performance Optimization, and Lifecycle Maintenance. International Journal of Research and Applied Innovations, 5(6), 7994-8003.
18. Sardana, A., Kotapati, V. B. R., & Shanmugam, L. (2020). AI-Guided Modernization Playbooks for Legacy Mission-Critical Payment Platforms. Journal of Artificial Intelligence & Machine Learning Studies, 4, 1-38.
19. Manda, P. (2023). LEVERAGING AI TO IMPROVE PERFORMANCE TUNING IN POST-MIGRATION ORACLE CLOUD ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8714-8725.
20. Kandula N (2023). Gray Relational Analysis of Tuberculosis Drug Interactions A Multi-Parameter Evaluation of Treatment Efficacy. J Comp Sci Appl Inform Technol. 8(2): 1-10.
21. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
22. Sridhar Kakulavaram. (2022). Life Insurance Customer Prediction and Sustainbility Analysis Using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 390 –.Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7649
23. Selvi, R., Saravan Kumar, S., & Suresh, A. (2014). An intelligent intrusion detection system using average manhattan distance-based decision tree. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 205-212). New Delhi: Springer India.
24. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
25. Arulraj AM, Sugumar, R., Estimating social distance in public places for COVID-19 protocol using region CNN, Indonesian Journal of Electrical Engineering and Computer Science, 30(1), pp.414-424, April 2023.
26. IBM. (2014). Cognitive computing: The next generation of enterprise intelligence. IBM Research White Paper.
