Articles Open Access
Autonomous AI Governance Framework for Real-Time Financial Transaction Processing in Cloud-Native Environments
Independent Researcher, USA
Abstract
Cloud-native infrastructures reshape business operations by offering elastic IT resources for real-time processing of financial transactions. Therefore, governance must bridge the seemingly contradictory objectives of regulatory compliance and agentic autonomy. The Agentic AI-Driven Governance Framework responds to these challenges, with a specific focus on real-time processing, using the analysis, design, and implement patterns of the Unifying Cybersecurity Framework. A complex adaptive system perspective captures interactions among the core components, securing overall behavior. Processing incidents has immediate repercussions for operation and reputation, and governance must therefore minimize incident impact and recovery time. Risk assessment quantifies the operational and financial impact of incidents.
Cloud-native infrastructures reshape business operations by offering elastic IT resources for real-time processing of financial transactions. Historical precedents, however, reveal that the introduction of such systems—especially those involving autonomous decision-making—poses governance challenges. Decision-making autonomy must, at a minimum, support and preferably enhance regulatory compliance objectives. Fully agentic AI-based systems, on the other hand, drive compliance and ethics to the background, further aggravating governance quality. With agentic governance, the objectives are richer and relate directly to operational risk management. Yet the very nature of agentic governance appears to undermine regulatory compliance, since compliance with rules and regulation is substituted by an accredited agentic policymaking process. Therefore, there seems to be an inherent tension between agentic governance and regulatory compliance, making the internal or external auditing of agentic systems a paramount concern.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 7 |
| Number | 6 |
| Pages | 13595-13608 |
| Published | 2023-11-15 |
| Issue | Vol. 7 No. 6 (2023): International Research Journal of Innovative Engineering (IRJIE) |
References
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50. 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.
51. 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.
52. 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.
53. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
54. 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.
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2. Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., & Tzoumas, K. (2015). Apache Flink: Stream and batch processing in a single engine. IEEE Data Engineering Bulletin, 38(4), 28–38.
3. Akidau, T., Bradshaw, R., Chambers, C., Chernyak, S., Fernández-Moctezuma, R. J., Lax, R., McVeety, S., Mills, D., Perry, F., Schmidt, E., & Whittle, S. (2015). The dataflow model: A practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing. Proceedings of the VLDB Endowment, 8(12), 1792–1803.
4. 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.
5. Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka: A distributed messaging system for log processing. Proceedings of the NetDB Workshop.
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.
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12. 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.
13. 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.
14. Davuluri, P. N. (2020). Improving Data Quality and Lineage in Regulated Financial Data Platforms. Finance and Economics, 1(1), 1-14.
15. 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.
16. 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.
17. 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.
18. Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
19. 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.
20. 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.
21. 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.
22. 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.
23. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
24. Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1-19.
25. 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.
26. 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.
27. 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.
28. 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.
29. 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.
30. Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
31. 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.
32. 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.
33. DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.
34. 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.
35. 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.
36. Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1-14.
37. Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
38. 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.
39. Reddy, V. A. R. (2022). Designing Fault-Tolerant Data Ingestion Pipelines for High-Volume Healthcare Transactions. Frontiers in Health Informatics, 11, 861-889.
40. 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.
41. 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.
42. 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.
43. 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.
44. 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
45. Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1-14.
46. 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.
47. Aitha, A. R. (2022). Cloud Native ETL Pipelines for Real Time Claims Processing in Large Scale Insurers. Universal Journal of Business and Management.
48. 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.
49. 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.
50. 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.
51. 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.
52. 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.
53. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
54. 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.
55. Dapper, a large-scale distributed systems tracing infrastructure. (2010). Proceedings of the ACM SIGOPS Symposium on Operating Systems Principles (SOSP).
56. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
57. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229.
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