Intelligent Cloud-Based Software Maintenance Architecture for Life Insurance Enterprises: Integrating AI, Gray Relational Analysis, and Risk-Aware Scalability across SAP and Oracle EBS Platforms

Main Article Content

Charlotte Elizabeth Kensington

Abstract

The rapid digital transformation of the life insurance sector has driven enterprises to adopt cloud-based platforms such as SAP and Oracle E-Business Suite (EBS) for managing mission-critical operations. However, maintaining these complex systems at scale introduces challenges related to security, scalability, risk optimization, and software maintenance efficiency. This study proposes an intelligent cloud-based software maintenance architecture that integrates Artificial Intelligence (AI) and Gray Relational Analysis (GRA) to enable predictive, adaptive, and ethically governed maintenance across heterogeneous enterprise environments. The framework employs machine learning algorithms for anomaly detection, failure prediction, and automated maintenance scheduling, while GRA supports multi-factor decision analysis to evaluate and prioritize maintenance and risk parameters across diverse modules. A risk-aware scalability model ensures optimal performance and resilience under large-scale deployments, supported by dynamic resource orchestration and cloud elasticity mechanisms. Security and ethical governance are embedded throughout the architecture to ensure transparency, data integrity, and regulatory compliance. Empirical validation in life insurance enterprise case studies demonstrates measurable improvements in system uptime, risk mitigation, and maintenance cost efficiency. The results establish a blueprint for intelligent, secure, and scalable enterprise maintenance, bridging AI-driven automation with quantitative decision analytics in complex cloud ecosystems.

Article Details

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Articles

How to Cite

Intelligent Cloud-Based Software Maintenance Architecture for Life Insurance Enterprises: Integrating AI, Gray Relational Analysis, and Risk-Aware Scalability across SAP and Oracle EBS Platforms. (2023). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9688-9691. https://doi.org/10.15662/IJRPETM.2023.0606008

References

1. Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.

2. Sugumar, R. (2022). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. IEEE 2 (2):1-6.

3. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.

4. 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.

5. 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.

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. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.

8. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the predictions of any classifier. Proceedings of KDD 2016.

9. 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.

10. Manda, P. (2023). Migrating Oracle Databases to the Cloud: Best Practices for Performance, Uptime, and Risk Mitigation. International Journal of Humanities and Information Technology, 5(02), 1-7.

11. Muthirevula, G. R., Kotapati, V. B. R., & Ponnoju, S. C. (2020). Contract Insightor: LLM-Generated Legal Briefs with Clause-Level Risk Scoring. European Journal of Quantum Computing and Intelligent Agents, 4, 1-31.

12. 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.

13. Anbalagan, B. (2023). Proactive Failover and Automation Frameworks for Mission-Critical Workloads: Lessons from Manufacturing Industry. International Journal of Research and Applied Innovations, 6(1), 8279-8296.

14. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.

15. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.

16. 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

17. 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.

18. Arul Raj .A.M and Sugumar R.,” Monitoring of the social Distance between Passengers in Real-time through video Analytics and Deep learning in Railway stations for Developing highest Efficiency” , March 2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022, ISBN 979- 835033384-8, March 2023, Chennai , India ., DOI 10.1109/ICDSAAI55433.2022.10028930.

19. European Commission. (2019). Ethics Guidelines for Trustworthy AI. Publications Office of the European Union.