AI at Scale in Enterprise Systems: Cloud-Native Architectures Cybersecurity Predictive Analytics and Intelligent Automation across Banking Retail Healthcare and Payments

Viktor Andreas Åkesson

doi:10.15662/IJRAI.2023.0605008

Authors

Viktor Andreas Åkesson Senior ML Engineer, Sweden Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0605008

Keywords:

AI at scale, Enterprise systems, Cloud-native architectures, Cybersecurity, Predictive analytics, Intelligent automation, Banking, Retail, Healthcare, Payments

Abstract

The rapid adoption of artificial intelligence (AI) at scale is transforming enterprise systems across multiple sectors, including banking, retail, healthcare, and payments. Cloud-native architectures provide the flexibility, scalability, and reliability necessary to deploy AI-driven solutions efficiently while ensuring robust cybersecurity and regulatory compliance. This paper explores the integration of predictive analytics, intelligent automation, and real-time decision-making to optimize operational efficiency and enhance risk management. Key applications include fraud detection in banking, personalized recommendations in retail, patient monitoring and interoperability in healthcare, and high-throughput transaction processing in payment systems. By leveraging AI at scale, enterprises can achieve faster insights, improve system resilience, and reduce operational costs. The paper also highlights future directions, including federated learning, explainable AI, and hybrid cloud deployments to support secure, transparent, and adaptive enterprise operations.

References

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

2. Kusumba, S. (2023). A Unified Data Strategy and Architecture for Financial Mastery: AI, Cloud, and Business Intelligence in Healthcare. International Journal of Computer Technology and Electronics Communication, 6(3), 6974-6981.

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. Sasidevi, J., Sugumar, R., & Priya, P. S. (2017). A Cost-Effective Privacy Preserving Using Anonymization Based Hybrid Bat Algorithm With Simulated Annealing Approach For Intermediate Data Sets Over Cloud Computing. International Journal of Computational Research and Development, 2(2), 173-181.

5. Meka, S. (2022). Streamlining Financial Operations: Developing Multi-Interface Contract Transfer Systems for Efficiency and Security. International Journal of Computer Technology and Electronics Communication, 5(2), 4821-4829.

6. 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.

7. Biggio, B., & Roli, F. (2018). Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84, 317–331.

8. 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.

9. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.

10. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.

11. Praveen Kumar Reddy Gujjala. (2022). Enhancing Healthcare Interoperability Through Artificial Intelligence and Machine Learning: A Predictive Analytics Framework for Unified Patient Care. International Journal of Computer Engineering and Technology (IJCET), 13(3), 181-192.

12. Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.

13. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.

14. Paul, D. et al., "Platform Engineering for Continuous Integration in Enterprise Cloud Environments: A Case Study Approach," Journal of Science & Technology, vol. 2, no. 3, Sept. 8, (2021). https://thesciencebrigade.com/jst/article/view/382

15. Vasugi, T. (2022). AI-Optimized Multi-Cloud Resource Management Architecture for Secure Banking and Network Environments. International Journal of Research and Applied Innovations, 5(4), 7368-7376.

16. 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.

17. Oleti, Chandra Sekhar. (2022). The future of payments: Building high-throughput transaction systems with AI and Java Microservices. World Journal of Advanced Research and Reviews. 16. 1401-1411. 10.30574/wjarr.2022.16.3.1281

18. 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.

19. 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

20. Rahwan, I. (2018). Society in the loop: Programming the algorithmic social contract. Ethics and Information Technology, 20(1), 5–14.

21. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.

22. Nagarajan, G. (2023). AI-Integrated Cloud Security and Privacy Framework for Protecting Healthcare Network Information and Cross-Team Collaborative Processes. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6292-6297.

23. Sugumar, R. (2016). Conditional Entropy with Swarm Optimization Approach for Privacy Preservation of Datasets in Cloud.

24. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.

25. Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation? International Data Privacy Law, 7(2), 76–99.