Distributed architecture and data resilience: the use of big data and machine learning in anomaly detection for financial transactions
Distributed architecture and data resilience: the use of big data and machine learning in anomaly detection for financial transactions
DOI:
https://doi.org/10.51473/rcmos.v1i1.2025.1957Keywords:
Distributed Architecture. Big Data. Machine Learning. Fraud Detection. Cloud Computing.Abstract
This article analyzes the evolution of software architecture within the financial context, focusing on the transition from monolithic systems to distributed microservices and their impact on transactional security. The research investigates how the integration of Big Data and Machine Learning algorithms in cloud computing environments allows for real-time fraud detection with low latency. The methodology addresses the challenges of the CAP Theorem, eventual consistency, and massive data stream processing. The results demonstrate that decoupled architectures, when combined with AI predictive models, offer superior resilience and a critical response capability for mitigating financial risks on a global scale.
Downloads
References
BREWER, E. A. CAP twelve years later: how the “rules” have changed. Computer, v. 45, n. 2, p. 23–29, 2012. DOI: https://doi.org/10.1109/MC.2012.37
NEWMAN, S. Building microservices: designing fine-grained systems. 2. ed. Sebastopol: O’Reilly Media, 2021.
KLEPPMANN, M. Designing data-intensive applications: the big ideas behind reliable, scalable, and maintainable systems. Sebastopol: O’Reilly Media, 2017.
AMAZON WEB SERVICES. Machine learning on AWS. [S. l.]: Amazon Web Services, 2025.
GHEMAWAT, S.; GOBIOFF, H.; LEUNG, S.-T. The Google file system. ACM SIGOPS Operating Systems Review, v. 37, n. 5, p. 29–43, 2003. DOI: https://doi.org/10.1145/1165389.945450
VERBA, N. et al. A review on fraud detection using machine learning techniques in the financial sector. International Journal of Computer Applications, v. 176, n. 1, p. 34–40, 2020.
RICHARDSON, C. Microservices patterns: with examples in Java. Shelter Island: Manning Publications, 2018.
KREPS, J. I heart logs: event data, stream processing, and data integration. Sebastopol: O’Reilly Media, 2014.
DEAN, J.; GHEMAWAT, S. MapReduce: simplified data processing on large clusters. Communications of the ACM, v. 51, n. 1, p. 107–113, 2008. DOI: https://doi.org/10.1145/1327452.1327492
SHOARAFI, A. et al. Real-time credit card fraud detection using machine learning algorithms. Journal of Supercomputing, v. 77, p. 1–25, 2021.
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Robson Alves dos Santos (Autor)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This means you are free to:
- Share — copy and redistribute the material in any medium or format.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The use of this material is conditioned upon proper attribution to the original author(s), providing a link to the license, and indicating if changes were made. The license does not require permission from the author or the publisher as long as these conditions are met.
The Creative Commons license logo is displayed permanently in the footer of the journal.
The copyright of the manuscript can be retained by the authors without restrictions and can be requested at any time, even after publication in the journal.
How to Cite
