Articles
Integrating Agentic AI and Generative AI into Cloud-Native DevOps Pipelines for Financial Payment Systems
Abstract
Modern financial systems deliver essential services in highly regulated, zero-failure environments. Yet unprecedented external pressures—geopolitical conflicts, economic instabilities, climate change, and the COVID-19 pandemic—have challenged the ability of financial regulators and institutions to maintain confidentiality, integrity, availability, and consistency of these systems. Financial services increasingly leverage automation enabled by Artificial Intelligence (AI) to enhance efficiency, manage demand, and mitigate risks, but in many deployments, the traditional risk-based approaches are no longer viable. Therefore, regulators and institutions are exploring the implications of automated operations.
The rationale is that AI-enabled automation and decision-making can be performed at greater velocity and scale than pure human workflows. However, the risk-management posture requires stringent governance. Financial institutions must clearly define and maintain human oversight in critical or harmful decision-making. For other responsibilities, the policy permitting automated decision-making needs to be captured, expressed, and securely stored in sources that can be read and interpreted by Generative AI for creating Operations Manuals and other organizational context documents, in order to enable LLMs to explain operations and processes, as well as to ensure that AI agents are trained in accordance with these policies.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 6 No. 6 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
2902-2917 |
Published |
December 14, 2023 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dhanaraj Sathiri (2023). Integrating Agentic AI and Generative AI into Cloud-Native DevOps Pipelines for Financial Payment Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 6 No. 6 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 2902-2917. https://doi.org/10.15662/IJAESIT.2023.0606009 |
References
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36. Zhi, Y., Wang, H., & Wang, L. (2022). A state of health estimation method for electric vehicle Li-ion batteries using GA-PSO-SVR. Complex & Intelligent Systems, 8, 2167–2182.
37. Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to. NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1-17.
38. DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
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48. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
49. Mashetty, S., & Mukesh, A. (2022). Application of Machine Learning in Loan Default Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 123-132. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P114
50. Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1-14.
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52. Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Universal Journal of Business and Management.
53. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.
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55. Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
56. Inala, R. (2022). Cross-Domain MDM Integration Using AI-Driven Data Governance: A Case Study In Financial Technology Architecture. Migration Letters, 19(2), 280-304.
57. Reddy, V. A. R. (2021). Challenges in Standardizing Member Eligibility Data Across Multi-Payer Healthcare Ecosystems. International Journal of Medical Toxicology and Legal Medicine, 24(3), 1-19.
58. Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. World Journal of Clinical Medicine Research, 1(1), 1-18.
59. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
60. Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019). Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews, 113, 109254.
61. Zhang, S., Zhang, X., & Chen, X. (2021). Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures. Journal of Power Sources, 506, 230132.
62. Mashetty, S. (2022). Enhancing Financial Data Security And Business Resiliency In Housing Finance: Implementing AI-Powered Data Analytics, Deep Learning, And Cloud-Based Neural Networks For Cybersecurity And Risk Management. Migration Letters, 19(6), 1302-1818.
2. Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
3. Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33–47.
4. Brito, L. C., Susto, G. A., Brito, J. N., & Duarte, M. A. V. (2022). An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. Mechanical Systems and Signal Processing, 163, 108105.
5. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
6. Loganathan, R. (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26.
7. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
8. Akbar, K., Zou, Y., Awais, Q., Baig, M. J. A., & Jamil, M. (2022). A machine learning-based robust state of health (SOH) prediction model for electric vehicle batteries. Electronics, 11(8), 1216.
9. Kolla, S. H. (2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research, 7(3), 11811.
10. Aitha, A. R. (2021). Dev Ops Driven Digital Transformation: Accelerating Innovation In The Insurance Industry. Journal of International Crisis and Risk Communication Research.
11. Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3), 2213–2227.
12. Inala, R. (2021). A New Paradigm in Retirement Solution Platforms: Leveraging Data Governance to Build AI-Ready Data Products. Journal of International Crisis and Risk Communication Research, 286-310.
13. Gao, Y., Li, C., & Huang, L. (2022). An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery. Frontiers in Energy Research, 10, 1032660.
14. Davuluri, P. N. (2020). Improving Data Quality and Lineage in Regulated Financial Data Platforms. Finance and Economics, 1(1), 1-14.
15. Chang, C., Wu, Y., Jiang, J., Jiang, Y., Tian, A., Li, T., & Gao, Y. (2022). Prognostics of the state of health for lithium-ion battery packs in energy storage applications. Energy, 239, 121986.
16. Someshwar Mashetty, “Data-driven insights for community investment through mortgage-backed securities,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI: 10.17148/IJIREEICE.2022.101214
17. Lee, G., Kim, J., & Lee, C. (2022). State-of-health estimation of Li-ion batteries in the early phases of qualification tests: An interpretable machine learning approach. Expert Systems with Applications, 197, 116817.
18. Reddy, V. A. R. (2022). Data-Driven Healthcare Operations: Architecting Unified Member, Provider, and Claims Intelligence Platforms. International Journal of Science, Research and Technology, 5(5), 8511-8521.
19. Ezemobi, E., Silvagni, M., Mozaffari, A., Tonoli, A., & Khajepour, A. (2022). State of health estimation of lithium-ion batteries in electric vehicles under dynamic load conditions. Energies, 15(3), 1234.
20. Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
21. Mattaparthi, R. (2021). Unified Data Lineage and Quality Governance Framework for Multi-Source Sensor Streams in Heavy-Duty Powertrain Manufacturing. Online Journal of Mechanical Engineering, 1(1), 1-15.
22. Barragán-Moreno, A., Schaltz, E., Gismero, A., & Stroe, D.-I. (2022). Capacity state-of-health estimation of electric vehicle batteries using machine learning and impedance measurements. Electronics, 11(9), 1414.
23. Zhang, H., He, X., Yan, W., Jiang, Z., & Zhu, S. (2022). A machine learning-based approach for product maintenance prediction with reliability information conversion. Autonomous Intelligent Systems, 2, 15.
24. Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587.
25. Manoharan, A., Begam, K. M., Aparow, V. R., & Sooriamoorthy, D. (2022). Artificial neural networks, gradient boosting and support vector machines for electric vehicle battery state estimation: A review. Journal of Energy Storage, 55, 105384.
26. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
27. Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., Bazant, M. Z., Harris, S. J., Chueh, W. C., & Braatz, R. D. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4(5), 383–391.
28. Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1-19.
29. Hu, X., Jiang, J., Cao, D., & Egardt, B. (2018). Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling. IEEE Transactions on Industrial Electronics, 63(4), 2645–2656.
30. Sharma, J., Mittal, M. L., & Soni, G. (2022). Condition-based maintenance using machine learning and role of interpretability: A review. Journal of Industrial Integration and Management.
31. Mukesh, A., & Aitha, A. R. (2021). Insurance Risk Assessment Using Predictive Modeling Techniques. International Journal of Emerging Research in Engineering and Technology, 2(4), 68-79.
32. Yao, L., Wen, J., Xu, S., Zheng, J., Hou, J., Fang, Z., & Xiao, Y. (2022). State of health estimation based on the long short-term memory network using incremental capacity and transfer learning. Sensors, 22(20), 7835.
33. Inala, R. (2020). Building Foundational Data Products for Financial Services: A MDM-Based Approach to Customer, and Product Data Integration. Universal Journal of Finance and Economics, 1(1), 1-18.
34. Feng, L., Jiang, L., Liu, J., Wang, Z., Wei, Z., & Wang, Q. (2021). Dynamic overcharge investigations of lithium ion batteries with different state of health. Journal of Power Sources, 507, 230262.
35. Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
36. Zhi, Y., Wang, H., & Wang, L. (2022). A state of health estimation method for electric vehicle Li-ion batteries using GA-PSO-SVR. Complex & Intelligent Systems, 8, 2167–2182.
37. Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to. NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1-17.
38. DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
39. Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., & Van den Bossche, P. (2016). Critical review of state of health estimation methods of Li-ion batteries for real applications. Renewable and Sustainable Energy Reviews, 56, 572–587.
40. Tong, Z., Miao, J., Tong, S., & Lu, Y. (2021). Early prediction of remaining useful life for lithium-ion batteries based on a hybrid machine learning method. Journal of Cleaner Production, 317, 128265.
41. Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1-14.
42. Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
43. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
44. Reddy, V. A. R. (2022). Designing Fault-Tolerant Data Ingestion Pipelines for High-Volume Healthcare Transactions. Frontiers in Health Informatics, 11, 861-889.
45. Ren, L., Zhao, L., Hong, S., Zhao, S., Wang, H., & Zhang, L. (2018). Remaining useful life prediction for lithium-ion battery: A deep learning approach. IEEE Access, 6, 50587–50598.
46. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
47. Li, A. G., Wang, W., West, A. C., & Preindl, M. (2022). Health and performance diagnostics in Li-ion batteries with pulse-injection-aided machine learning. Applied Energy, 315, 119056.
48. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
49. Mashetty, S., & Mukesh, A. (2022). Application of Machine Learning in Loan Default Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 123-132. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P114
50. Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1-14.
51. Mattaparthi, R. (2022). Engineering Predictive Industrial Systems Through IoT-Driven Asset Monitoring and Machine Learning Prognostics. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7790.
52. Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Universal Journal of Business and Management.
53. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.
54. Zraibi, B., Mansouri, M., & Loukili, S. E. (2022). Comparing deep learning methods to predict the remaining useful life of lithium-ion batteries. Materials Today: Proceedings, 62, 6298–6304.
55. Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
56. Inala, R. (2022). Cross-Domain MDM Integration Using AI-Driven Data Governance: A Case Study In Financial Technology Architecture. Migration Letters, 19(2), 280-304.
57. Reddy, V. A. R. (2021). Challenges in Standardizing Member Eligibility Data Across Multi-Payer Healthcare Ecosystems. International Journal of Medical Toxicology and Legal Medicine, 24(3), 1-19.
58. Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. World Journal of Clinical Medicine Research, 1(1), 1-18.
59. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
60. Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019). Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews, 113, 109254.
61. Zhang, S., Zhang, X., & Chen, X. (2021). Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures. Journal of Power Sources, 506, 230132.
62. Mashetty, S. (2022). Enhancing Financial Data Security And Business Resiliency In Housing Finance: Implementing AI-Powered Data Analytics, Deep Learning, And Cloud-Based Neural Networks For Cybersecurity And Risk Management. Migration Letters, 19(6), 1302-1818.