Next-Generation Digital Ecosystems for AI Optimization Across Finance and Healthcare and Critical Infrastructure and Digital Marketing

Main Article Content

Peter Jonathan Hampstead

Abstract

The emergence of next-generation digital ecosystems is transforming operational efficiency, strategic decision-making, and customer engagement across critical industries. This paper presents a comprehensive framework for AI-driven optimization spanning finance, healthcare, critical infrastructure, and digital marketing, highlighting how integrated digital platforms can unify data, analytics, and business processes. By leveraging cloud-native architectures, AI algorithms, and SAP-enabled enterprise systems, organizations can achieve enhanced operational agility, predictive intelligence, and robust governance.
 

The proposed approach demonstrates how digital ecosystems can harmonize structured and unstructured data, facilitate cross-domain collaboration, and deliver actionable insights in real time. AI models are utilized for risk assessment, predictive analytics, marketing personalization, and critical operations monitoring, while platform governance ensures compliance, security, and scalability. The framework underscores the importance of interoperable systems, data quality, and continuous AI-driven feedback loops to maximize value across multiple domains. This work provides a roadmap for enterprises to leverage AI within integrated digital ecosystems to drive innovation, efficiency, and strategic advantage.

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How to Cite

Next-Generation Digital Ecosystems for AI Optimization Across Finance and Healthcare and Critical Infrastructure and Digital Marketing. (2024). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11181-11188. https://doi.org/10.15662/IJRPETM.2024.0705007

References

1. Humble, J., Farley, D., Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation, Addison-Wesley, 2010.

2. Bussu, V. R. R. (2023). Governed Lakehouse Architecture: Leveraging Databricks Unity Catalog for Scalable, Secure Data Mesh Implementation. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6298-6306.

3. Kumar, S. S. (2023). AI-Based Data Analytics for Financial Risk Governance and Integrity-Assured Cybersecurity in Cloud-Based Healthcare. International Journal of Humanities and Information Technology, 5(04), 96-102.

4. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.

5. Karnam, A. (2024). Next-Gen Observability for SAP: How Azure Monitor Enables Predictive and Autonomous Operations. International Journal of Computer Technology and Electronics Communication, 7(2), 8515–8524. https://doi.org/10.15680/IJCTECE.2024.0702006

6. Weaveworks, GitOps: Operations by Pull Request, Weaveworks White Paper, 2017.

7. Burns, B., Grant, B., Oppenheimer, D., Brewer, E., Wilkes, J., “Borg, Omega, and Kubernetes,” ACM Queue, vol. 14, no. 1, 2016.

8. Ramakrishna, S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6282-6291.

9. Udayakumar, R., Joshi, A., Boomiga, S. S., & Sugumar, R. (2023). Deep fraud Net: A deep learning approach for cyber security and financial fraud detection and classification. Journal of Internet Services and Information Security, 13(3), 138-157.

10. Armbrust, M., et al., “Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores,” Proceedings of the VLDB Endowment, vol. 13, no. 12, 2020.

11. Databricks, The Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics, White Paper, 2021.

12. Kasaram, C. R. (2023). Harnessing Asynchronous Patterns with Event Driven Kafka and Microservices Architectures. Journal of Artificial Intelligence & Cloud Computing, 2(4), 1-4.

13. Gartner, AIOps Platforms Improve IT Operations Through Analytics and Machine Learning, Gartner Research Report, 2022.

14. Nagarajan, G. (2024). A Cybersecurity-First Deep Learning Architecture for Healthcare Cost Optimization and Real-Time Predictive Analytics in SAP-Based Digital Banking Systems. International Journal of Humanities and Information Technology, 6(01), 36-43.

15. Kumar, R. K. (2024). Real-time GenAI neural LDDR optimization on secure Apache–SAP HANA cloud for clinical and risk intelligence. IJEETR, 8737–8743. https://doi.org/10.15662/IJEETR.2024.0605006

16. 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.

17. 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.

18. 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

19. Chandramohan, A. (2017). Exploring and overcoming major challenges faced by IT organizations in business process improvement of IT infrastructure in Chennai, Tamil Nadu. International Journal of Mechanical Engineering and Technology, 8(12), 254.

20. 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

21. Chivukula, V. (2023). Calibrating Marketing Mix Models (MMMs) with Incrementality Tests. International Journal of Research and Applied Innovations (IJRAI), 6(5), 9534–9538.

22. SAP SE, SAP Business Technology Platform (BTP): Architecture and Services Overview, SAP Documentation, 2023.

23. 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.

24. 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.

25. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.

26. Vengathattil, Sunish. 2021. "Interoperability in Healthcare Information Technology – An Ethics Perspective." International Journal For Multidisciplinary Research 3(3). doi: 10.36948/ijfmr.2021.v03i03.37457.

27. 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.

28. Mahajan, N. (2024). AI-Enabled Risk Detection and Compliance Governance in Fintech Portfolio Operations. Cuestiones de Fisioterapia, 53(03), 5366-5381.

29. SAP SE, SAP Datasphere and Data Governance Capabilities, SAP Technical White Paper, 2023.

30. Kim, G., Debois, P., Willis, J., Humble, J., The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security, IT Revolution Press, 2016.

31. 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.

32. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3 (5), 44–53.

33. Venkatachalam, D., Paul, D., & Selvaraj, A. (2022). AI/ML powered predictive analytics in cloud-based enterprise systems: A framework for scalable data-driven decision making. Journal of Artificial Intelligence Research, 2(2), 142–182.

34. Zaharia, M., et al., “Apache Spark: A Unified Engine for Big Data Processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.