Autonomous Finance, Native Cloud, Agentic Core
Keywords:
Agentic Artificial Intelligence in Finance, Reinforcement Learning for Trading Systems, Automated Trading and Risk Management, DevOps-Driven Financial AI, Cloud-Native Financial Architectures, Continuous Model Deployment (MLOps), Governance-Embedded Infrastructure, Regulatory-Compliant AI Systems, Real-Time Model Adaptation, Supervisory Technology (SupTech) Alignment, Scalable Financial AI Operations, Observability in Automated Trading Systems, Model Lifecycle Management, Secure and Resilient Financial Platforms, AI Governance and Control Frameworks.Abstract
Conventional automated trading and risk management employ deterministic models within a well-defined decision support paradigm. Emerging DevOps-driven cloud-native architectures, however, enable scalable, resilient, operable, observant, and automated systems where models are continuously updated in response to changing real-world conditions. Recent research suggests extending agent-based reinforcement learning beyond a decision support role toward direct decision-making responsibilities, underpinned by proper control and governance principles. The combination of such agentic models within automated financial systems presents novel challenges, while fulfilling requirements defined by supervisory authorities. A suitably architected cloud-native environment thus has the potential to not only expedite the delivery of financial AI models, but also to support their increasingly frequent deployment and operation with minimum effort and oversight. The specific considerations, from foundational concepts through to development and operational constraints, then govern the underlying implementation.
The ultimate goal is a DevOps-driven cloud-native implementation framework that guarantees the reliable delivery of agentic AI models into automated trading and risk management systems, quickly, frequently, and with minimal overhead. In this highly regulated domain, controls must therefore be embedded within the supporting infrastructure and processes rather than bolted on afterwards. The key metrics for measuring success then become the speed, frequency, and simplicity with which models are delivered, continuously improved, and retired, all while remaining compliant with a multitude of ever-present security, risk, governance, and operational requirements. By explicitly specifying these considerations, the intention is to present a coherent synthesis of the architectural rationale and a repeatable implementation approach.
References
1. Mills, N., Issadeen, Z., Matharaarachchi, A., Bandaragoda, T., De Silva, D., & Jennings, A. (2024). A cloud-based architecture for explainable big data analytics using self-structuring artificial intelligence. Discover Artificial Intelligence, 4(33), 1–22.
2. Villar, E., Martín Toral, I., Calvo, I., Barambones, O., & Fernández-Bustamante, P. (2024). Architectures for industrial AIoT applications. Sensors, 24(15), 4929.
3. Sanepalli, U. R. (2024). Enterprise lakehouse architecture for customer analytics: AI and machine learning—Synchronized ingestion and compute optimization. World Journal of Advanced Research and Reviews, 23(2), 2949–2959.
4. Mangala, N. (2024). Leveraging Microsoft Fabric lakehouse as an AI-ready data platform for enterprise analytics. Journal of Information Systems Engineering and Management, 9(4s), 1–15.
5. Mariotti, L., Guidetti, V., Mandreoli, F., Belli, A., & Lombardi, P. (2024). Combining large language models with enterprise knowledge graphs: A perspective on enhanced natural language understanding. Frontiers in Artificial Intelligence, 7, 1460065.
6. Uzhakova, N., & Fischer, S. (2024). Data-driven enterprise architecture for pharmaceutical R&D. Digital, 4(2), 333–371.
7. Hasan, M. (2024). Enhancing enterprise security with zero trust architecture. arXiv Preprint arXiv:2410.18291.
8. Fernandez-Carames, T. M., Blanco-Novoa, O., Froiz-Miguez, I., & Fraga-Lamas, P. (2024). Towards an autonomous industry 4.0 warehouse: A UAV and blockchain-based system for inventory and traceability applications in big data-driven supply chain management. arXiv Preprint arXiv:2402.00709.
9. Felsenbruch, O. M. (2024). Policy driven and zero trust AI architectures for secure data lakes and fraud detection and migration and real-time enterprise intelligence. International Journal of Research Publications in Engineering, Technology and Management, 7(5), 11203–11210.
10. Dohmke, T. (2024). Secure federated AI and cloud data engineering systems for scalable enterprise intelligence and governance platforms. International Journal of Research Publications in Engineering, Technology and Management, 7(6), 11688–11694.
11. Rousseau, C. A. (2024). A cloud-native enterprise framework enabling AI-driven automation governance secure networks mobile platforms and ethical decision intelligence. International Journal of Research and Applied Innovations, 7(5), 1–10.
12. Mani, R. (2024). AI enabled cloud architectures for intelligent secure and resilient enterprise systems with autonomous decision intelligence. International Journal of Computer Technology and Electronics Communication, 7(6), 9923–9931.
13. Rossi, M. A. (2024). A cloud-native and AI-driven architecture for inclusive digital public services with broadband connectivity enterprise MLOps and SAP platforms. International Journal of Computer Technology and Electronics Communication, 7(5), 1–12.
14. Shea, E. M. O. (2024). Fault tolerant AI-cloud architecture for healthcare, finance, and agriculture: ML, NLP, and disease analytics integrated with SAP ERP. International Journal of Research and Applied Innovations, 7(6), 11807–11816.
15. Wang, H., Li, Y., & Zhang, X. (2024). Enterprise generative AI governance: Frameworks, challenges, and implementation strategies. IEEE Access, 12, 112345–112360.
16. Kumar, R., Singh, P., & Verma, A. (2024). AI-driven enterprise data integration architectures for scalable analytics. Future Internet, 16(3), 87–104.
17. Chen, J., Zhao, L., & Xu, Y. (2024). Cloud-native data platforms for enterprise AI applications. Software: Practice and Experience, 54(5), 945–967.
18. Park, S., Kim, H., & Lee, J. (2024). Explainable AI architectures for enterprise decision intelligence systems. Applied Sciences, 14(8), 3241.
19. Brown, T., Wilson, M., & Evans, K. (2024). Enterprise knowledge graph architectures for large-scale AI deployment. Information Systems Frontiers, 26(4), 1251–1270.
20. Ahmad, S., Khan, M., & Rahman, F. (2024). Federated learning architectures for secure enterprise analytics. Journal of Big Data, 11(1), 66.
21. Liu, Y., Zhang, H., & Chen, W. (2024). Multi-cloud enterprise architectures for AI-enabled digital transformation. Computers, 13(6), 145.
22. Silva, R., Pereira, J., & Costa, A. (2024). Data mesh adoption in enterprise analytics ecosystems. Data & Knowledge Engineering, 151, 102315.
23. Gupta, N., Sharma, P., & Jain, R. (2024). Intelligent data governance frameworks for enterprise AI platforms. Information Systems Management, 41(3), 211–228.
24. Roberts, D., Miller, S., & Young, P. (2024). MLOps architectures for enterprise-scale machine learning systems. Journal of Systems and Software, 210, 111893.
25. Tang, Z., Li, X., & Wu, C. (2024). AI-ready data lakes for enterprise analytics modernization. Electronics, 13(9), 1738.
26. Garcia, M., Lopez, J., & Sanchez, A. (2024). Secure cloud-native architectures for enterprise intelligence applications. Future Generation Computer Systems, 156, 330–344.
27. Ibrahim, A., Hassan, R., & Ali, M. (2024). Autonomous enterprise decision systems using reinforcement learning. Expert Systems with Applications, 248, 123456.
28. Patel, V., Shah, D., & Mehta, K. (2024). Enterprise AI adoption: Architecture, governance, and scalability challenges. Information, 15(4), 202.
29. Nguyen, T., Tran, H., & Le, Q. (2024). Event-driven architectures for enterprise AI platforms. Concurrency and Computation: Practice and Experience, 36(12), e8124.
30. Johnson, E., Clark, R., & Green, D. (2024). AI-enabled enterprise integration using microservices and APIs. IEEE Software, 41(4), 52–61.
31. Wang, L., Zhou, Y., & Sun, J. (2024). Semantic enterprise architectures for AI-powered analytics. Knowledge-Based Systems, 295, 111745.
32. Torres, F., Alvarez, J., & Ruiz, P. (2024). Enterprise data fabrics: Architectural patterns and implementation strategies. Computer Standards & Interfaces, 89, 103812.
33. O’Brien, M., Kelly, S., & Murphy, P. (2024). Digital transformation through AI-centric enterprise architectures. Technology in Society, 78, 102541.
34. Lee, K., Choi, J., & Park, H. (2024). Scalable enterprise analytics using lakehouse architectures. Journal of Cloud Computing, 13(1), 58.
35. Ahmed, N., Islam, S., & Rahman, T. (2024). Enterprise cybersecurity architectures integrating AI and zero trust principles. Computers & Security, 139, 103692.
36. Becker, A., Schneider, M., & Hoffmann, R. (2024). Enterprise AI governance and compliance frameworks in regulated industries. Business & Information Systems Engineering, 66(5), 587–603.
37. Singh, A., Kumar, V., & Tiwari, S. (2024). Intelligent workflow orchestration in enterprise AI ecosystems. Journal of Network and Computer Applications, 235, 104011.
38. Martins, P., Ferreira, L., & Gomes, R. (2024). Hybrid cloud architectures for enterprise AI workloads. Cluster Computing, 27(4), 9851–9867.
39. Zhao, Q., Lin, Y., & Huang, J. (2024). Enterprise-scale retrieval augmented generation architectures. IEEE Access, 12, 156421–156438.
40. White, C., Adams, G., & Roberts, H. (2024). Data observability frameworks for AI-enabled enterprise platforms. Information Systems, 122, 102355.
41. Das, S., Roy, P., & Ghosh, A. (2024). AI-driven enterprise modernization through cloud-native architectures. International Journal of Information Management Data Insights, 4(2), 100251.
42. Kim, Y., Lee, D., & Jeong, S. (2024). Knowledge-centric enterprise architectures using large language models. Applied Artificial Intelligence, 38(1), 2387654.
43. Rivera, J., Morales, P., & Castillo, A. (2024). Enterprise automation architectures powered by generative AI. Automation in Construction, 164, 105484.
44. Chandra, R., Verma, S., & Yadav, N. (2024). AI-enabled enterprise integration platforms for cross-domain analytics. International Journal of Intelligent Systems, 39(7), e23112.
45. Peterson, B., Lewis, T., & Hall, J. (2024). Responsible AI architectures for enterprise applications. AI and Ethics, 4(3), 923–939.
46. Li, M., Xu, Z., & Gao, F. (2024). Enterprise graph intelligence architectures for advanced analytics. Information Sciences, 675, 120789.
47. Svensson, P., Andersson, L., & Bergstrom, J. (2024). Scalable enterprise platforms for AI-driven business intelligence. Decision Support Systems, 184, 114296.
48. Rahman, M., Karim, A., & Chowdhury, S. (2024). Distributed AI architectures for enterprise data ecosystems. Journal of Big Data, 11(1), 84.
49. Foster, R., Campbell, D., & Morgan, S. (2024). Enterprise architecture patterns for trustworthy artificial intelligence systems.
Additional Files
Published
Issue
Section
License
The American Online Journal of Science and Engineering (AOJSE) is an open-access journal, and all published articles are made freely available to readers worldwide under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license governs the use, sharing, adaptation, and distribution of all content published in AOJSE and is designed to promote the broadest possible dissemination and reuse of scholarly research.
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Under the Creative Commons Attribution 4.0 International License, any user is free to share, copy, and redistribute the published material in any medium or format, and to adapt, remix, transform, and build upon the material for any purpose, including commercial use, provided that appropriate credit is given to the original authors and source. The license cannot be revoked as long as the terms are followed.
When using or redistributing content published in AOJSE, users must provide proper attribution by citing the original authors' names, the article title, the journal name (AOJSE), the volume and issue number, the year of publication, and the DOI or URL of the article. Users must also indicate if any changes were made to the original work. Attribution must be provided in a reasonable manner but must not suggest that the authors or AOJSE endorse the user or their use of the material.
Author Rights and Copyright Retention
AOJSE respects and upholds the intellectual property rights of all contributing authors. Authors who publish in AOJSE retain full copyright over their work. By submitting a manuscript to AOJSE, authors grant the journal a non-exclusive, worldwide, royalty-free license to publish, reproduce, distribute, publicly display, and archive the article in print and electronic formats. This license allows AOJSE to make the article freely accessible to readers globally while ensuring that the authors remain the rightful owners of their work.
Authors are permitted to deposit their published articles in institutional repositories, personal websites, academic networking platforms, and preprint servers, provided that the original publication in AOJSE is acknowledged and properly cited. Authors may also reuse their published content in subsequent works, presentations, teaching materials, and grant applications without requiring prior permission from the journal.
Third-Party Content
Authors are solely responsible for obtaining written permission to reproduce any third-party material, including figures, tables, images, data, or excerpts from other publications, included in their manuscript. Evidence of such permissions must be provided to the editorial office upon request. AOJSE does not assume any responsibility for copyright infringement arising from the unauthorized inclusion of third-party content in published articles.
Permitted Uses
Under the CC BY 4.0 license, the following uses of AOJSE published content are freely permitted without prior written permission from the journal or authors, provided that proper attribution is given. Users may read, download, print, and distribute articles for personal, educational, or research purposes. Researchers may reuse data, figures, and findings for meta-analyses, systematic reviews, and secondary research. Educators may incorporate published articles into course materials, syllabi, and academic presentations. Journalists, policymakers, and practitioners may reference and cite published findings for professional and public interest purposes.
Prohibited Uses
While the CC BY 4.0 license is broad and permissive, users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Users may not misrepresent the original authorship of published content or imply endorsement by the original authors or AOJSE without explicit consent. Plagiarism, data fabrication, and any form of academic misconduct in the reuse of published material are strictly prohibited and are a violation of publication ethics standards.
Institutional and Repository Deposit Policy
AOJSE fully supports self-archiving and green open access. Authors are encouraged to deposit the final published version of their article, also known as the version of record, in institutional repositories, subject repositories, and open-access databases immediately upon publication. There is no embargo period. Authors should always link back to the original article on the AOJSE website and include the full citation and DOI when depositing their work in any repository.
Digital Preservation and Archiving
AOJSE is committed to the long-term digital preservation of all published content to ensure its permanent availability and accessibility. The journal utilizes the Open Journal Systems (OJS) platform, which supports internationally recognized digital archiving standards. AOJSE encourages participation in archiving networks such as LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled LOCKSS) to safeguard published articles against data loss and ensure continued access for future generations of researchers and readers.
Disclaimer
The views, opinions, findings, and conclusions expressed in articles published in AOJSE are solely those of the authors and do not necessarily reflect the official position, policy, or views of the journal, its editorial board, or its affiliated organizations. AOJSE makes every effort to ensure the accuracy and integrity of published content but does not accept legal responsibility for any errors, omissions, or claims arising from the use of information published in the journal.
Contact for Licensing Inquiries
For any questions regarding the licensing terms, copyright, or permitted use of content published in AOJSE, please contact the editorial office through the journal website at https://aojse.org. The editorial team will be happy to assist authors, readers, and institutions with any licensing-related queries.
