Intelligent Cloud-Native CI/CD Framework with SAP and AI and Machine Learning for Predictive Enterprise Engineering in Insurance and Finance
DOI:
https://doi.org/10.15662/IJRAI.2025.0804010Keywords:
Cloud-Native CI/CD, SAP Integration, Predictive Enterprise Engineering, Artificial Intelligence, Machine Learning, Insurance Systems, Financial Platforms, DevSecOps, Business IntelligenceAbstract
The rapid digital transformation of insurance and financial services has increased the need for scalable, resilient, and intelligent enterprise engineering practices. Traditional CI/CD pipelines, while effective for automation, lack predictive and adaptive capabilities required in highly regulated, risk-sensitive environments. This paper proposes an intelligent cloud-native CI/CD framework integrating SAP platforms with artificial intelligence (AI) and machine learning (ML) to enable predictive enterprise engineering. The framework embeds AI-driven analytics across the software delivery lifecycle to support proactive risk detection, performance forecasting, intelligent testing, and autonomous optimization. By leveraging SAP cloud ecosystems and machine learning models, the proposed approach aligns business objectives, regulatory compliance, and operational resilience. The framework demonstrates how predictive insights can transform CI/CD pipelines from reactive automation tools into strategic enablers of business agility and risk-aware digital transformation in insurance and financial enterprises.References
1. Beyer, B., Jones, C., Petoff, J., & Murphy, N. R. (2016). Site reliability engineering: How Google runs production systems. O’Reilly Media.
2. Bussu, V. R. R. (2024). End-to-End Architecture and Implementation of a Unified Lakehouse Platform for Multi-ERP Data Integration using Azure Data Lake and the Databricks Lakehouse Governance Framework. International Journal of Computer Technology and Electronics Communication, 7(4), 9128-9136.
3. Balaji, K. V., & Sugumar, R. (2023, December). Harnessing the Power of Machine Learning for Diabetes Risk Assessment: A Promising Approach. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-6). IEEE.
4. Humble, J., & Farley, D. (2010). Continuous delivery: Reliable software releases through build, test, and deployment automation. Addison-Wesley.
5. Vasugi, T. (2022). AI-Enabled Cloud Architecture for Banking ERP Systems with Intelligent Data Storage and Automation using SAP. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4319-4325.
6. Kim, G., Debois, P., Willis, J., & Humble, J. (2016). The DevOps handbook: How to create world-class agility, reliability, and security in technology organizations. IT Revolution Press.
7. Kumar, S. S. (2024). SAP-Based Digital Banking Architecture Using Azure AI and Deep Learning for Real-Time Healthcare Predictive Analytics. International Journal of Technology, Management and Humanities, 10(02), 77-88.
8. SAP SE. (2023). SAP business technology platform: Architecture and integration overview. SAP Press.
9. Nagarajan, G. (2024). Cloud-Integrated AI Models for Enhanced Financial Compliance and Audit Automation in SAP with Secure Firewall Protection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(1), 9692-9699.
10. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
11. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
12. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
13. Ramakrishna, S. (2024). Intelligent Healthcare and Banking ERP on SAP HANA with Real-Time ML Fraud Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(Special Issue 1), 1-7.
14. Paul, D., Sudharsanam, S. R., & Surampudi, Y. (2021). Implementing Continuous Integration and Continuous Deployment Pipelines in Hybrid Cloud Environments: Challenges and Solutions. Journal of Science & Technology, 2(1), 275-318.
15. Meka, S. (2023). Empowering Members: Launching Risk-Aware Overdraft Systems to Enhance Financial Resilience. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7517-7525.
16. Navandar, P. (2023). Guarding Networks: Understanding the Intrusion Detection System (IDS). Journal of biosensors and bioelectronics research. https://d1wqtxts1xzle7.cloudfront.net/125806939/20231119-libre.pdf?1766259308=&response-content-disposition=inline%3B+filename%3DGuarding_Networks_Understanding_the_Intr.pdf&Expires=1767147182&Signature=H9aJ73csgfALZ~2B89oBRyYgz57iuooJUU0zKPdjpmQjunvziuvJjd~r8gYT52Ah6RozX-LUpFB14VO8yjXrVD73j1HN9DAMi1PSGKaRbcI8gBbrnFQQGOhTO7VYkGcz3ylDLZJatGabbl5ASNiqe0kINjsw6op5mJzXUoWLZkmret8YBzR1b6Ai8j4SCuZ2kc75dAfryQSZDKuv9ISFi9oHyMxEwWKkyNDnnDP~0EW3dBp7qmwPJVbnm7wSQFFU9AUx5o3T742k80q8ZxvS8M-63TZkyb5I3oq6zBUOCVgK471hm2K9gYtYPrwePdoeEP5P4WmIBxeygrqYViN9nw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
17. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
18. Rahman, M. R., Rahman, M., Rasul, I., Arif, M. H., Alim, M. A., Hossen, M. S., & Bhuiyan, T. (2024). Lightweight Machine Learning Models for Real-Time Ransomware Detection on Resource-Constrained Devices. Journal of Information Communication Technologies and Robotic Applications, 15(1), 17-23.
19. Hossain, A., ataur Rahman, K., Zerine, I., Islam, M. M., Hasan, S., & Doha, Z. (2023). Predictive Business Analytics For Reducing Healthcare Costs And Enhancing Patient Outcomes Across US Public Health Systems. Journal of Medical and Health Studies, 4(1), 97-111.
20. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
21. Al Rafi, M. (2023). Machine learning–enhanced predictive marketing analytics for optimizing customer engagement and sales forecasting. International Journal of Research and Applied Innovations, 6(4), 9203-9213.
22. Kavuru, L. T. (2024). Generative AI as a Project Stakeholder: Shifting Team Dynamics and Decision Making Power in 2024. International Journal of Research and Applied Innovations, 7(6), 11775-11783.
23. Kumar, S. N. P. (2022). Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 67–79. https://ijhit.info/index.php/ijhit/article/view/140/136
24. Joyce, S., Pasumarthi, A., & Anbalagan, B. (2025). SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURENATIVE TOOLS AND PRACTICES.||.
25. Kasaram, C. R. (2023). Harnessing Asynchronous Patterns with Event Driven Kafka and Microservices Architectures. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1-4.
26. Sharma, A., Borovica-Gajic, R., Lee, S., & Banerjee, A. (2021). Machine learning for cloud operations: A survey. ACM Computing Surveys, 53(6), 1–37. https://doi.org/10.1145/3459992
27. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
28. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
29.
30. Thambireddy, S. (2021). Enhancing Warehouse Productivity through SAP Integration with Multi-Model RF Guns. International Journal of Computer Technology and Electronics Communication, 4(6), 4297-4303.
31. Malarkodi, K. P., Sugumar, R., Baswaraj, D., Hasan, A., & Kousalya, A. (2023, March). Cyber Physical Systems: Security Technologies, Application and Defense. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 2536-2546). IEEE.
32. Rajurkar, P. (2024). Integrating AI in Air Quality Control Systems in Petrochemical and Chemical Manufacturing Facilities. International Journal of Innovative Research of Science, Engineering and Technology, 13(10), 17869 - 17873.
33. Adari, V. K. (2024). The Path to Seamless Healthcare Data Exchange: Analysis of Two Leading Interoperability Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11472-11480.
34. Zhang, Q., Chen, M., Li, L., & Li, H. (2020). Predictive analytics for DevOps and continuous delivery. IEEE Software, 37(4), 55–62. https://doi.org/10.1109/MS.2020.2986785





