The week's pick
Reseach Article
Real-Time Resilience: Scaling Financial Risk Assessment with Event-Driven Cloud Architectures
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 102 |
| Year of Publication: 2026 |
| Authors: Sriramprabhu Rajendran |
10.5120/ijca6d0eaed5a6ec
|
Sriramprabhu Rajendran . Real-Time Resilience: Scaling Financial Risk Assessment with Event-Driven Cloud Architectures. International Journal of Computer Applications. 187, 102 ( May 2026), 41-45. DOI=10.5120/ijca6d0eaed5a6ec
Abstract
This paper examines the use of Event-Driven Architecture (EDA) patterns to improve the optimization of financial risk evaluation in a distributed cloud-based system of finance. Today's financial system is characterized by a number of difficulties in processing high-speed data feeds in a timely manner, ensuring sub-millisecond latency and high availability. This paper proposes a decoupled system utilizing distributed event brokers and stream processors to identify market anomalies and credit risks in a timely fashion. This research utilizes a risk data set of 404 unique risk scenarios, including high-frequency trading (HFT) simulation data and credit transaction data, to measure system efficiency. The system environment utilizes Apache Kafka for event streaming, Kubernetes for cloud orchestration, and Prometheus for monitoring. The results show that event-driven architecture can improve system efficiency by eliminating traditional request-response processing bottlenecks. Furthermore, by utilizing distributed ledgers and serverless architecture, financial organizations can improve their risk profile granularity. The results show that by utilizing reactive programming, financial organizations can improve their risk management approach by shifting their traditional reactive approach to a proactive approach.
References
- J. Luna, A. Taha, R. Trapero, and N. Suri, “Quantitative Reasoning About Cloud Security Using Service Level Agreements,” IEEE Transactions on Cloud Computing, pp. 1–1, 2015. DOI: http://dx.doi.org/10.1109/TCC.2015.2469659
- E. Cayirci, A. Garaga, A. Santana, and Y. Roudier, “A Cloud Adoption Risk Assessment Model,” in 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 908–913, 2014. DOI:10.1109/UCC.2014.148
- E. Cayirci, “A joint trust and risk model for MSaaS mashups,” in 2013 Winter Simulations Conference (WSC), pp. 1347–1358, 2013. doi: http://dx.doi.org/10.1109/WSC.2013.6721521
- S. M. Habib, V. Varadharajan, and M. Muhlhauser, “A Trust-Aware Framework for Evaluating Security Controls of Service Providers in Cloud Marketplaces,” in 2013 IEEE 12th International Conference on Trust, Security and Privacy in Computing and Communications, pp. 459–468, 2013. DOI: http://dx.doi.org/10.1109/TrustCom.2013.58
- S. M. Habib, S. Ries, and M. Muhlhauser, “Towards a Trust Management System for Cloud Computing,” in 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications, pp. 933–939, 2011. DOI: http://dx.doi.org/10.1109/TrustCom.2011.129
- J. Luna, R. Langenberg, and N. Suri, “Benchmarking cloud security level agreements using quantitative policy trees,” in Proceedings of the 2012 ACM Workshop on Cloud Computing Security Workshop, pp. 103–112, 2012. DOI: http://dx.doi.org/10.1145/2381913.2381932
- N. Kratzke and P.-C. Quint, “Understanding cloud-native applications after 10 years of cloud computing: a systematic mapping study,” Journal of Systems and Software, vol. 126, pp. 1–16, 2017. DOI: https://doi.org/10.1016/j.jss.2017.01.001
- K. Priyadarsini, E. F. I. Raj, A. Y. Begum, and V. Shanmugasundaram, “Comparing DevOps procedures from the context of a systems engineer,” Materials Today: Proceedings, 2020. DOI: https://doi.org/10.1016/j.matpr.2020.09.624
- O. Tomarchio, D. Calcaterra, and G. D. Modica, “Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks,” Journal of Cloud Computing, vol. 9, p. 49, 2020. DOI: https://doi.org/10.1186/s13677-020-00194-7
- M. Liaqat, V. Chang, A. Gani, S. H. A. Hamid, M. Toseef, U. Shoaib, and R. L. Ali, “Federated cloud resource management: review and discussion,” Journal of Network and Computer Applications, vol. 77, pp. 87–105, 2017. DOI: https://doi.org/10.1016/j.jnca.2016.10.008
- M. Chiregi and N. Jafari Navimipour, “Cloud computing and trust evaluation: a systematic literature review of the state-of-the-art mechanisms,” Journal of Electrical Systems and Information Technology, vol. 5, pp. 608–622, 2018. DOI: https://doi.org/10.1016/j.jesit.2017.09.001
- M. Alexandre, T. C. Silva, C. Connaughton, and F. A. Rodrigues, “The drivers of systemic risk in financial networks: a data-driven machine learning analysis,” Chaos, Solitons & Fractals, vol. 152, p. 111588, 2021. DOI: https://doi.org/10.1016/j.chaos.2021.111588
- A. Vijayalakshmi and Hridya, “Functionalities and approaches of multi-cloud environment,” in Operationalizing Multi-Cloud Environments, Springer, 2022, pp. 257–268. https://link.springer.com/chapter/10.1007/978-3-030-74402-1_1
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