Intelligent Cloud-Native Infrastructure for Financial Transactions
Keywords:
Cloud-native, microservices, streaming-data, event-driven, fraud-detection, compliance, risk-scoring, scaling, disruptive-technology, governance, privacy, model-risk, AI-Architecture, supervisory-tech.Abstract
AI-powered cloud-native financial platforms based on microservices architectures enable secure, real-time, and scalable transaction processing for payment, insurance, brokerage, trading, capital markets, and treasury services. Cloud-native architectures are designed to support the convergence of digital banking, cybersecurity, fraud detection, capital market trading, trading platform services, liquidity management, data-driven pricing, and governmental regulatory compliance. Cloud environments are also the preferred foundation for integrating the machine learning workloads behind AI innovation into financial-function operations.
The primary platform enabler is real-time transaction-processing capability, essential for providing privacy risk scoring, instantaneous payment fraud detection, and market-shock liquidity forecasting with embedded pricing models. Cloud-native streaming-data architecture, event-driven processing with near-real-time latency guarantees, and the consistency–availability–partition tolerance framework underpin secure processing of financial transactions. AI methods supporting these platforms incorporate transaction anomaly detection for fraud prevention, risk scoring for the governing approval process, ASIC-generated cash–liquidity forecasting for funding cost management, and data-driven algorithms for pricing bank, insurance, and brokerage products.
References
1. Imerman, M., Patel, R., & Kim, Y.-D. (2022). Cloud finance: A review and synthesis of cloud computing and cloud security in financial services. Journal of Financial Transformation, 55, 18–25.
2. Challa, K. (2021). Cloud native architecture for scalable fintech applications with real time payments. International Journal of Engineering and Computer Science, 10(12), 25501–25515.
3. Haseeb, M., Geng, J., Duclos-Cavalcanti, D., Butler, U., Hao, X., Sivaraman, A., & Narayana, S. (2024). Design and implementation of a scalable financial exchange in the public cloud. arXiv.
4. Pereira, K., Vinagre, J., Alonso, A. N., Coelho, F., & Carvalho, M. (2022, September). Privacy-preserving machine learning in life insurance risk prediction. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 44-52). Cham: Springer Nature Switzerland.
5. Zhang, Y., Zhou, W., Ren, Y., Li, S., Li, G., & Yu, G. (2023). Towards Transaction as a Service. arXiv.
6. Wang, Y., Zhu, M., Yuan, J., Wang, G., & Zhou, H. (2024). The intelligent prediction and assessment of financial information risk in the cloud computing model. arXiv.
7. Li, B., Peng, X., Xiang, Q., et al. (2022). Enjoy your observability: An industrial survey of microservice tracing and analysis. Empirical Software Engineering, 27, Article 25.
8. Mondal, S. K., Wu, X., Kabir, H. M. D., Dai, H. N., Ni, K., & Yuan, H. (2023). Toward optimal load prediction and customizable autoscaling scheme for Kubernetes. Mathematics, 11(12), 2675.
9. Lu, C.-H. (2024). The moderating role of e-lifestyle on disclosure intention in mobile banking: A privacy calculus perspective. Electronic Commerce Research and Applications, 64, 101374.
10. Soni, H., Perla, S., Maddela, S., & Kumar, U. (2025, November). Generative AI in Cloud CRM: Securing Intelligent Workflows in Multi-Cloud Environments. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1-9). IEEE.
11. Abusitta, A., et al. (2024). Survey on explainable AI: Techniques, challenges and open issues. Expert Systems with Applications, 255, 124710.
12. Zhu, L., et al. (2024). Two-stage learning approach for semantic-aware task scheduling in container-based clouds. Expert Systems with Applications.
13. Qiao, Y., et al. (2024). EdgeOptimizer: A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters. Future Generation Computer Systems, 156, 221–230.
14. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
15. Jiang, J., et al. (2024). Deep reinforcement learning enhanced microservice scheduling on Kubernetes clusters in cloud-edge environments. IEEE Transactions on Cloud Computing.
16. Shahin, M., Babar, M. A., & Zhu, L. (2021). Continuous integration, delivery and deployment: A systematic review on approaches, tools, challenges and practices. IEEE Access.
17. Ashok, S., Harsh, V., Godfrey, P. B., Mittal, R., Parthasarathy, S., & Schwartz, L. (2024). TraceWeaver: Distributed request tracing for microservices without application modification. In Proceedings of the ACM SIGCOMM Conference.
18. Kolla, S. K. (2022). Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5370-5380. |
19. Zhang, S.-L., Xia, S., Fan, W., Shi, B., Xiong, X., Zhong, Z., Ma, M., Sun, Y., & Pei, D. (2024). Failure diagnosis in microservice systems: A comprehensive survey and analysis. ACM Transactions on Software Engineering and Methodology.
20. Chatterjee, P. (2023). Cloud-native architecture for high-performance payment system. TIJER – International Research Journal, 10(4), 345–358.
21. Dodda, A. (2021). From legacy to innovation: Architecting cloud-based AI and blockchain solutions for secure and intelligent financial transactions. Journal of International Crisis and Risk Communication Research.
22. Kolla, S. H. (2024). Retrieval-Augmented Enterprise Intelligence: Enhancing Accuracy, Trust, and Operational Decision-Making. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12425.
23. Dhanorkar, T., Ponnoju, S. C., & Kunju, S. S. (2024). Cloud-native wallet fabric: Engineering scalable, multicurrency e-wallet platforms. Journal of Artificial Intelligence and Global Systems.
24. Challa, K. (2021). Event-driven cloud-native payment systems for enterprise fintech platforms. International Journal of Engineering and Computer Science, 10(12), 25501–25515.
25. Imerman, M., Patel, R., & Kim, Y.-D. (2022). Cloud computing adoption and cybersecurity considerations in financial services. Journal of Financial Transformation, 55, 18–25.
26. Reddy, V. A. R. (2023). Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7979-7992.
27. Haseeb, M., Geng, J., Duclos-Cavalcanti, D., Butler, U., Hao, X., Sivaraman, A., & Narayana, S. (2024). Design and implementation of a scalable financial exchange in the public cloud. arXiv.
28. Zhang, Y., Zhou, W., Ren, Y., Li, S., Li, G., & Yu, G. (2023). Towards transaction as a service. arXiv.
29. Wang, Y., Zhu, M., Yuan, J., Wang, G., & Zhou, H. (2024). The intelligent prediction and assessment of financial information risk in the cloud computing model. arXiv.
30. Mondal, S. K., Wu, X., Kabir, H. M. D., Dai, H. N., Ni, K., & Yuan, H. (2023). Toward optimal load prediction and customizable autoscaling scheme for Kubernetes. Mathematics, 11(12), 2675.
31. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
32. Li, B., Peng, X., Xiang, Q., et al. (2022). Enjoy your observability: An industrial survey of microservice tracing and analysis. Empirical Software Engineering, 27, Article 25.
33. Shahin, M., Babar, M. A., & Zhu, L. (2021). Continuous integration, delivery, and deployment practices: A systematic review. IEEE Access, 9, 26138–26159.
34. Qiao, Y., Zhang, H., Li, X., & Chen, Y. (2024). EdgeOptimizer: A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters. Future Generation Computer Systems, 156, 221–230.
35. Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
36. Jiang, J., Li, Y., Wang, H., & Chen, X. (2024). Deep reinforcement learning enhanced microservice scheduling on Kubernetes clusters in cloud-edge environments. IEEE Transactions on Cloud Computing.
37. Abusitta, A., et al. (2024). Explainable artificial intelligence: Techniques, challenges, and future directions. Expert Systems with Applications, 255, 124710.
38. Zhu, L., Wang, Y., Chen, X., & Li, H. (2024). Two-stage learning approach for semantic-aware task scheduling in container-based cloud systems. Expert Systems with Applications.
39. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
40. Ashok, S., Harsh, V., Godfrey, P. B., Mittal, R., Parthasarathy, S., & Schwartz, L. (2024). TraceWeaver: Distributed request tracing for microservices without application modification. In Proceedings of the ACM SIGCOMM Conference.
41. Zhang, S. L., Xia, S., Fan, W., Shi, B., Xiong, X., Zhong, Z., Ma, M., Sun, Y., & Pei, D. (2024). Failure diagnosis in microservice systems: A comprehensive survey and analysis. ACM Transactions on Software Engineering and Methodology.
42. Lu, C. H. (2024). The moderating role of e-lifestyle on disclosure intention in mobile banking: A privacy calculus perspective. Electronic Commerce Research and Applications, 64, 101374.
43. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
44. Nagraj, A. (2023). Cloud-native architectures in financial services: Enhancing scalability and security with AWS and Kubernetes. Journal of Computer Science and Technology Studies, 5(4), 296–308.
45. Eddula, D. (2024). Financial inclusion through scalable cloud-native transaction systems. International Journal of Intelligent Systems and Applications in Engineering.
46. Sanepalli, U. R. (2023). Cognitive goal-driven financial infrastructure: A cloud-native, AI-orchestrated architecture for investment trade settlement and risk management systems. World Journal of Advanced Research and Reviews, 19(1), 1659–1667.
47. Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
48. Imerman, M., Patel, R., & Kim, Y.-D. (2022). Cloud finance: A review of cloud computing adoption and cybersecurity in financial services. Journal of Financial Transformation, 55, 18–25.
49. Challa, K. (2021). Cloud-native architecture for scalable fintech applications with real-time payments. International Journal of Engineering and Computer Science, 10(12), 25501–25515.
50. Chatterjee, P. (2023). Cloud-native architecture for high-performance payment systems. TIJER – International Research Journal, 10(4), 345–358.
51. Reddy, V. A. R., & Kolla, S. K. (1984). Infrastructure-As-Code Practices For Regulated Healthcare Cloud Environments. Metallurgical and Materials Engineering, 30 (4), 1028–1042.
52. Dhanorkar, T., Ponnoju, S. C., & Kunju, S. S. (2024). Cloud-native wallet fabric: Engineering scalable multicurrency e-wallet platforms. Journal of Artificial Intelligence and Global Systems.
53. Jiao, Q., Wang, X., Zhang, Y., & Li, H. (2022). Design of cloud native application architecture based on Kubernetes. In 2022 2nd International Conference on Computer Engineering and Application (ICCEA).
54. Zhang, Y., Zhou, W., Ren, Y., Li, S., Li, G., & Yu, G. (2023). Towards transaction as a service. arXiv.
55. Haseeb, M., Geng, J., Duclos-Cavalcanti, D., Butler, U., Hao, X., Sivaraman, A., & Narayana, S. (2024). Design and implementation of a scalable financial exchange in the public cloud. arXiv.
56. Reddy, V. A. R., & Kolla, S. K. (1984). Infrastructure-As-Code Practices For Regulated Healthcare Cloud Environments. Metallurgical and Materials Engineering, 30 (4), 1028–1042.
57. Wang, Y., Zhu, M., Yuan, J., Wang, G., & Zhou, H. (2024). The intelligent prediction and assessment of financial information risk in the cloud computing model. arXiv.
58. Qiao, Y., Zhang, H., Li, X., & Chen, Y. (2024). EdgeOptimizer: A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters. Future Generation Computer Systems, 156, 221–230.
59. Zhu, L., Wang, Y., Chen, X., et al. (2024). Two-stage learning approach for semantic-aware task scheduling in container-based clouds. Expert Systems with Applications.
60. Lu, C.-H. (2024). The moderating role of e-lifestyle on disclosure intention in mobile banking: A privacy calculus perspective. Electronic Commerce Research and Applications, 64, 101374.
61. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
62. Abusitta, A., et al. (2024). Survey on explainable AI: Techniques, challenges and open issues. Expert Systems with Applications, 255, 124710.
63. Challa, K. (2021). Cloud native architecture for scalable fintech applications with real time payments. International Journal of Engineering and Computer Science, 10(12), 25501–25515.
64. Dodda, A. (2021). From legacy to innovation: Architecting cloud-based AI and blockchain solutions for secure and intelligent financial transactions. Journal of International Crisis and Risk Communication Research, 4(S4), 21–44.
65. Peddi, R. K. (2024). AI-Based Workforce Analytics for SLA Governance and Uptime Assurance in Data Centers. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8589-8601.
66. Eddula, D. (2026). Financial inclusion through scalable cloud-native transaction systems. International Journal of Intelligent Systems and Applications in Engineering.
67. Dhanorkar, T., Ponnoju, S. C., & Kunju, S. S. (2024). Cloud-native wallet fabric: Engineering scalable, multicurrency e-wallet platforms. Journal of Artificial Intelligence and Global Systems.
68. Mosali, S. R. (2024). Cloud-native architectures in financial services: A comprehensive analysis of AI workload scaling and fraud detection. International Journal of Research in Computer Applications and Information Technology.
69. Nagraj, A. (2023). Cloud-native architectures in financial services: Enhancing scalability and security with AWS and Kubernetes. Journal of Computer Science and Technology Studies, 5(4), 296–308.
70. Joodala, A. (2024). Case study cloud-native trade finance platform for global banking operations. Journal of Computational Analysis and Applications, 33(8), 8103–8121.
71. Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
72. Jiang, J., et al. (2025). DRKC: Deep reinforcement learning enhanced microservice scheduling on Kubernetes clusters in cloud-edge environment. IEEE Transactions on Cloud Computing.
73. Li, B., Peng, X., Xiang, Q., et al. (2022). Enjoy your observability: An industrial survey of microservice tracing and analysis. Empirical Software Engineering, 27, Article 25.
74. Mondal, S. K., Wu, X., Kabir, H. M. D., Dai, H. N., Ni, K., & Yuan, H. (2023). Toward optimal load prediction and customizable autoscaling scheme for Kubernetes. Mathematics, 11(12), 2675.
75. Shahin, M., Babar, M. A., & Zhu, L. (2021). Continuous integration, delivery, and deployment: A systematic review on approaches, tools, challenges, and practices. IEEE Access.
76. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
77. Ashok, S., Harsh, V., Godfrey, P. B., Mittal, R., Parthasarathy, S., & Schwartz, L. (2024). TraceWeaver: Distributed request tracing for microservices without application modification. In Proceedings of the ACM SIGCOMM Conference.
78. Zhang, S.-L., Xia, S., Fan, W., Shi, B., Xiong, X., Zhong, Z., Ma, M., Sun, Y., & Pei, D. (2024). Failure diagnosis in microservice systems: A comprehensive survey and analysis. ACM Transactions on Software Engineering and Methodology.
79. Bandi, V. D. V. K. (2023). MLOps frameworks for reliable model deployment in cloud data platforms. Journal of Artificial Intelligence and Big Data, 3(1), 84-101.
80. Deng, S., Zhao, H., Huang, B., Zhang, C., Chen, F., Deng, Y., Yin, J., Dustdar, S., & Zomaya, A. Y. (2023). Cloud-native computing: A survey from the perspective of services. arXiv.
81. Pourmajidi, W., Zhang, L., Steinbacher, J., Erwin, T., & Miranskyy, A. (2023). A reference architecture for governance of cloud native applications. arXiv.
82. Haseeb, M., Geng, J., Duclos-Cavalcanti, D., Butler, U., Hao, X., Sivaraman, A., & Narayana, S. (2024). Design and implementation of a scalable financial exchange in the public cloud. arXiv.
83. Amistapuram, K. (2024). Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning. Available at SSRN 6143426.
84. Imerman, M., Patel, R., & Kim, Y.-D. (2022). Cloud finance: A review and synthesis of cloud computing and cloud security in financial services. Journal of Financial Transformation, 55, 18–25.
85. Nagraj, A. (2023). Cloud-native architectures in financial services: Enhancing scalability and security with AWS and Kubernetes. Journal of Computer Science and Technology Studies, 5(4), 296–308.
86. Kolla, T. (2024). Graph Neural Networks for HCC Risk Adjustment and Interoperability. International Journal of Science, Research and Technology, 7(6), 13244-13255.
87. Challa, K. (2021). Cloud native architecture for scalable fintech applications with real time payments. International Journal of Engineering and Computer Science, 10(12), 25501–25515.
88. Taluri, R. (2021). Cloud-native architectures for enterprise financial data management, analytics, and regulatory reporting compliance. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 101–116.
89. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
90. Adari, V. K. (2024). How cloud computing is facilitating interoperability in banking and finance. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 851–859.
91. Joodala, A. (2024). Case study cloud-native trade finance platform for global banking operations. Journal of Computational Analysis and Applications, 33(8), 8103–8121.
92. Das, R. G., & Pawa, R. (2024). Cloud in developing native applications. International Journal of Research Publication and Reviews, 5(3), 7253–7258.
93. Mangala, N. (2024). Leveraging Microsoft Fabric lakehouse as an AI-ready data platform for enterprise analytics. Journal of Information Systems Engineering and Management.
94. Richard, K., & Musoni, W. (2024). Exploring the design and development of cloud-native applications. International Journal of Innovative Science and Research Technology, 9(2).
95. Ahmed, M. I. (2024). Cloud-Native DevOps. Apress.
96. Bartlett, J. (2023). Cloud Native Applications with Docker and Kubernetes. Apress.
97. Goniwada, S. R. (2022). Cloud Native Architecture and Design. Apress.
98. Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
99. Sharma, R., & Atyab, M. (2022). Cloud-Native Microservices with Apache Pulsar. Apress.
100. Camposo, G. (2021). Cloud Native Integration with Apache Camel. Apress.
101. Chakraborty, M., & Kundan, A. P. (2021). Monitoring Cloud-Native Applications. Apress.
102. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
103. Chandramouli, R. (2023). Emerging technologies in cloud native application infrastructures. National Institute of Standards and Technology (NIST IR 8463).
104. Chandramouli, R. (2024). Service mesh proxy models for cloud-native applications (NIST SP 800-233). National Institute of Standards and Technology.
105. AI Driven Cloud Native Systems for Secure Financial Healthcare and Enterprise Analytics. (2024). International Journal of Computer Technology and Electronics Communication, 7(4), 9192–9199.
106. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 European Advanced Journal for Science & Engineering (EAJSE)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Articles published in the European Advanced Journal for Science & Engineering (EAJSE) are made freely available online immediately upon publication under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited. Authors retain copyright of their work. By submitting to EAJSE, authors grant the journal the right of first publication. For details, visit: https://creativecommons.org/licenses/by/4.0/
