Machine Learning Enabled Financial Security Architectures for Cloud-Native APIs and Enterprise Platforms with Big Data Fraud Analytics
Abstract
The rapid digitization of financial services, driven by cloud-native architectures and API-first enterprise platforms, has significantly expanded the threat landscape for fraud and cyber-attacks. Traditional rule-based security systems are increasingly inadequate against sophisticated, adaptive adversaries leveraging automation and distributed attack vectors. This study explores machine learning (ML)–enabled financial security architectures designed for cloud-native APIs and enterprise ecosystems, integrating big data fraud analytics to enhance detection accuracy, scalability, and resilience. The research synthesizes architectural principles from zero-trust security models, distributed microservices environments, and real-time data streaming frameworks to propose a layered, intelligent security framework. Leveraging techniques such as anomaly detection, supervised classification, graph analytics, and behavioral biometrics, the proposed architecture enables dynamic risk scoring and adaptive response mechanisms. The integration of cloud security services, container orchestration, and secure API gateways ensures continuous monitoring and automated mitigation across hybrid and multi-cloud deployments. Furthermore, the study evaluates architectural performance in terms of latency, throughput, false-positive rates, and regulatory compliance. The findings demonstrate that ML-enabled security architectures significantly enhance fraud detection capabilities while maintaining operational efficiency, offering a scalable blueprint for financial institutions navigating digital transformation in complex enterprise environments
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 5 No. 6 (2022): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
10416-10426 |
Published |
November 12, 2022 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Julien Ponge (2022). Machine Learning Enabled Financial Security Architectures for Cloud-Native APIs and Enterprise Platforms with Big Data Fraud Analytics. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 5 No. 6 (2022): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 10416-10426. https://doi.org/10.15662/IJAESIT.2022.0506003 |
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