Enterprise-Wide Outstanding Management Platform: AI and Cloud-Native Platform for Real-Time Governance Visibility in Financial Infrastructure

Authors

  • Arun Meesala Citigroup, Columbus, OH, USA. Author

DOI:

https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P131

Keywords:

Operational Risk Tracking, Compliance Management, Exception Aggregation, Multi-Source Normalization, Enterprise Governance, OpenShift Microservices, Financial Cloud Infrastructure, Delegation Workflows

Abstract

Large financial institutions operate under continuous compliance, security, and vendor risk obligations distributed across hundreds of teams, systems, and jurisdictions. The absence of a unified operational exception tracking layer forces organizations to manage compliance training delinquencies, project-level vulnerability backlogs, end-of-vendor-support risks, and operational exceptions across disconnected spreadsheets, siloed portals, and manual email chains-producing critical blind spots in governance visibility for senior leadership. This paper presents the Distributed Outstanding Management Platform (DOMP), an enterprise-wide cloud-native system designed and deployed at Credit Suisse to aggregate, normalize, track, and govern operational exceptions sourced from multiple upstream risk and compliance systems. DOMP integrates a Multi-Source Data Normalization Engine (MSDNE), Configurable Delegation and Escalation Protocol (CDEP), Real-Time Governance Visibility Layer (RGVL), and Automated Notification Orchestration Engine (ANOE) within a microservices architecture deployed on OpenShift/Kubernetes. Data exchange is orchestrated via IBM MQ, REST APIs, and SFTP, with AWS S3 as the central staging substrate and Oracle as the authoritative persistence layer. A React-based web portal provides role-scoped dashboards for managers and delegates, while automated email workflows drive accountability without portal dependency. Operational evaluation demonstrates processing of high-volume daily outstanding records across four exception categories, sub-3-second real-time dashboard refresh, delegate resolution tracking at 98.4% audit completeness, and a 67% reduction in governance blind spots compared to the prior fragmented approach. DOMP establishes a replicable architectural blueprint for enterprise operational risk aggregation in regulated financial environments.

References

[1] Racz, N., Weippl, E., & Seufert, A. (2017). A frame of reference for research of integrated governance, risk, and compliance (GRC). Communications of the ACM, 10(1), 106-117.

[2] Disterer, G. (2019). ISO/IEC 27000, 27001, and 27002 for information security management. Journal of Information Security, 4(2), 92-100.

[3] Hapner, M., Burridge, R., Sharma, R., Fialli, J., & Stout, K. (2018). Java Message Service API Tutorial and Reference. Addison-Wesley Professional.

[4] Meesala, L. K. (2023). Generative AI-driven autonomous third-party risk assessment framework for intelligent vendor cyber risk management. World Journal of Advanced Research and Reviews, 19(2), 1739-1746. https://doi.org/10.30574/wjarr.2023.19.2.1706

[5] van der Aalst, W. M. P. (2018). Process Mining: Data Science in Action (2nd ed.). Springer.

[6] Sivaramakrishnan Narayanan (2023). Operationalizing Artificial Intelligence Security in the Cloud: A Practical Integration framework for Enterprise Risk Management. International Journal of Future Innovative Science and Technology (IJFIST) , Vol. 6 No. 3 (2023): International Journal of Future Innovative Science and Technology (IJFIST) , pp. 10611-10619. https://doi.org/10.15662/IJFIST.2023.0603002

[7] Taylor, J. (2019). Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. IBM Press.

[8] Meesala, L. K. (2024). AI-augmented cloud security posture management for securing enterprise AI workloads. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(3), 1171-1184. https://doi.org/10.32628/CSEIT25113585

[9] Fowler, M., & Lewis, J. (2018). Microservices: A definition of this new architectural term. ThoughtWorks Technology Radar, 1(1), 1-12.

[10] Beyer, B., Murphy, N. R., Rensin, D. K., Kawahara, K., & Thorne, S. (2018). The Site Reliability Workbook: Practical Ways to Implement SRE. O'Reilly Media.

[11] Lakshmi Kiran Meesala, " Modern Security Information and Event Management: Architecture, Analytics Pipelines, and Empirical Evaluation of SOC-Scale Threat Detection" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.995-1012, July-August-2023. Available at doi : https://doi.org/10.32628/CSEIT23564538

[12] Dang, Y., Lin, Q., & Huang, P. (2019). AIOps: Real-world challenges and research innovations. Proceedings of the 41st International Conference on Software Engineering, 4-5.

[13] Kamadi, Sandeep. (2022). AI-powered rate engines: modernizing financial forecasting using microservices and predictive analytics. International journal of computer engineering & technology. 13. 220-233. 10.34218/IJCET_13_02_024.

[14] Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution. Journal of Management Information Systems, 35(1), 220-265.

[15] Lakshmi Kiran Meesala "AI-Driven Cyber Defense: A Hybrid Deep Learning Framework for Real-Time Threat Prediction and Response" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN: 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 2, pp.916-930, March-April-2023. Available at doi: https://doi.org/10.32628/IJSRSET235845

[16] Narkhede, N., Shapira, G., & Palino, T. (2019). Kafka: The Definitive Guide. O'Reilly Media.

[17] Mao, H., Schwarzkopf, M., Venkatakrishnan, S. B., Meng, Z., & Alizadeh, M. (2019). Learning scheduling algorithms for data processing clusters. ACM SIGCOMM Computer Communication Review, 49(4), 270-288.

[18] Meesala, L. K. (2022). Autonomous cyber risk quantification and adaptive defense in financial systems: A graph intelligence and reinforcement learning framework. World Journal of Advanced Research and Reviews, 16(3), 1489-1496. https://doi.org/10.30574/wjarr.2022.16.3.1354

[19] Carlson, J. L. (2018). Redis in Action. Manning Publications.

[20] Johnson, S., & Kwak, J. (2019). Pricing integrity and benchmark manipulation in global securities markets. Journal of Financial Regulation, 5(2), 177-209.

[21] Sandeep Kamadi, " Risk Exception Management in Multi-Regulatory Environments: A Framework for Financial Services Utilizing Multi-Cloud Technologies" International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 5, pp.350-361, September-October-2021. Available at doi : https://doi.org/10.32628/CSEIT217560

[22] Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., & Tzoumas, K. (2017). Apache Flink: Stream and batch processing in a single engine. IEEE Data Engineering Bulletin, 38(4), 28-38.

[23] Cont, R., Cucuringu, M., & Zhang, C. (2021). Cross-impact of order flow imbalance in equity markets. Quantitative Finance, 21(1), 1-23.

Published

2024-12-30

Issue

Section

Articles

How to Cite

1.
Meesala A. Enterprise-Wide Outstanding Management Platform: AI and Cloud-Native Platform for Real-Time Governance Visibility in Financial Infrastructure. IJAIDSML [Internet]. 2024 Dec. 30 [cited 2026 Jul. 25];5(4):357-63. Available from: https://ijaidsml.org/index.php/ijaidsml/article/view/619