Cloud-Native Enterprise DevOps Platforms with AI-Enhanced Fraud Detection for Financial Transactions and Secure API Ecosystems
DOI:
https://doi.org/10.15662/IJEETR.2023.0504005Keywords:
Cloud-native architecture, DevOps, DevSecOps, AI-enhanced fraud detection, financial transactions, secure APIs, Kubernetes, CI/CD, microservices, machine learning, API security, fintech security, containerization, zero trust architecture, real-time analyticsAbstract
Cloud-native enterprise DevOps platforms are transforming financial technology infrastructures by enabling scalable, resilient, and secure digital ecosystems. The rapid expansion of online financial services, mobile banking, and digital payment platforms has intensified the need for real-time fraud detection and secure API-based integrations. Cloud-native architectures—built on microservices, containers, Kubernetes orchestration, and Infrastructure as Code (IaC)—provide elasticity, fault tolerance, and rapid deployment capabilities. When integrated with AI-enhanced fraud detection systems, these platforms enable real-time transaction monitoring, adaptive anomaly detection, and predictive risk scoring. Secure API ecosystems further ensure seamless yet protected interoperability between financial institutions, fintech partners, and third-party service providers. However, deploying AI-driven fraud detection within cloud-native DevOps environments introduces challenges such as data governance complexities, model drift, API vulnerabilities, compliance risks, and security automation gaps in CI/CD pipelines. This study examines the architectural components, operational frameworks, and governance models that support secure, scalable financial transaction processing. It evaluates DevSecOps practices, API security mechanisms, and machine learning pipelines in enterprise contexts. The research contributes a structured methodology for integrating AI fraud detection within cloud-native DevOps platforms while maintaining regulatory compliance, operational resilience, and continuous delivery. The findings highlight both strategic advantages and implementation challenges in modern financial ecosystems.
References
1. Ananth, S., Kalpana, A. M., & Vijayarajeswari, R. (2020). A dynamic technique to enhance quality of service in software-defined network-based wireless sensor network using machine learning. International Journal of Wavelets, Multiresolution and Information Processing, 18(1), 1941020.
2. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research, 4(5), 5342–5351.
3. Vimal Raja, G. (2021). Mining customer sentiments from financial feedback and reviews using data mining algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705–14710.
4. Hebbar, K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology, 4(2), 401–414.
5. Kamadi, S. (2021). Risk exception management in multi-regulatory environments: A framework for financial services utilizing multi-cloud technologies.
6. Panda, M. R., & Kondisetty, K. (2022). Predictive fraud detection in digital payments using ensemble learning. American Journal of Data Science and Artificial Intelligence Innovations, 2, 673–707.
7. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations, 4(2), 4913–4920.
8. Singh, A. (2021). Evaluating reliability in mission-critical communication: Methods and metrics. International Journal of Innovative Research in Computer and Technology, 7(2), 1–11.
9. Muthusamy, P., Mohammed, A. S., & Ramalingam, S. (2021). Cloud-Native Customer Data Platforms (CDP): Optimizing Personalization Across Brands. American Journal of Autonomous Systems and Robotics Engineering, 1, 200-233.
10. Genne, S. (2022). Designing accessibility-first enterprise web platforms at scale. International Journal of Research and Applied Innovations, 5(5), 7679–7690.
11. Nagarajan, C., Neelakrishnan, G., Akila, P., Fathima, U., & Sneha, S. (2022). Performance analysis and implementation of 89C51 controller based solar tracking system with boost converter. Journal of VLSI Design Tools & Technology, 12(2), 34–41.
12. Prasanna, D., & Santhosh, R. (2018). Time orient trust based hook selection algorithm for efficient location protection in wireless sensor networks using frequency measures. International Journal of Engineering & Technology, 7(3.27), 331–335.
13. Perla, S. (2022). Innovating Salesforce with artificial intelligence and automation. International Journal of Communication Networks and Information Security, 14(2), 716–723. http://researchgate.net/profile/Srikanth-Perla-2/publication/391454725_Innovating_Salesforce_with_Artificial_Intelligence_and_Automation/links/6818e9c1bfbe974b23c30aba/Innovating-Salesforce-with-Artificial-Intelligence-and-Automation.pdf
14. Sreekala, K., Rajkumar, N., Sugumar, R., Sagar, K. D., Shobarani, R., Krishnamoorthy, K. P., & Yeshitla, A. (2022). Skin diseases classification using hybrid AI based localization approach. Computational Intelligence and Neuroscience, 2022(1), 6138490.
15. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IoT-based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.
16. Gaddapuri, N. S. (2021). Big data storage observation system. Power System Protection and Control, 49(2), 7–19.
17. Anumula, S. R. (2022). Transparent and auditable decision-making in enterprise platforms. International Journal of Research and Applied Innovations, 5(5), 7691–7702.
18. Keezhadath, A. A., Kota, R. K., & Selvaraj, A. (2021). Dynamic pricing optimization for global hospitality: Real-time data integration and decision making. American Journal of Autonomous Systems and Robotics Engineering, 1, 131–165.
19. Surisetty, L. S. (2023). Proactive Threat Mitigation in API Ecosystems through AI-Powered Anomaly Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(1), 7633-7642.
20. Gangina, P. (2022). Resilience engineering principles for distributed cloud-native applications under chaos. International Journal of Computer Technology and Electronics Communication, 5(5), 5760–5770.
21. Inbavalli, M., & Arasu, T. (2015). Efficient analysis of frequent item set association rule mining methods. International Journal of Scientific & Engineering Research, 6(4).
22. Navandar, P. (2022). SMART: Security model adversarial risk-based tool. International Journal of Research and Applied Innovations, 5(2), 6741–6752.
23. Anand, L., & Neelanarayanan, V. (2019). Feature selection for liver disease using particle swarm optimization algorithm. International Journal of Recent Technology and Engineering, 8(3), 6434–6439.
24. Murugamani, C., Saravanakumar, S., Prabakaran, S., & Kalaiselvan, S. A. (2015). Needle insertion on soft tissue using set of dedicated complementarily constraints. Advances in Environmental Biology, 9(22 S3), 144–149.
25. Ponugoti, M. (2022). Integrating API-first architecture with experience-centric design for seamless insurance platform modernization. International Journal of Humanities and Information Technology, 4(1–3), 117–136.
26. Vaidya, S., Shah, N., Shah, N., & Shankarmani, R. (2020, May). Real-time object detection for visually challenged people. In Proceedings of the International Conference on Intelligent Computing and Control Systems (pp. 311–316). IEEE.
27. Chennamsetty, C. S. (2023). Standardizing Software Delivery: Unified Data Models and Scalable Infrastructure for Subscription Ecosystems. International Journal of Computer Technology and Electronics Communication, 6(2), 6658-6665.
28. Inampudi, R. K., Pichaimani, T., & Surampudi, Y. (2022). AI-enhanced fraud detection in real-time payment systems: Leveraging machine learning and anomaly detection to secure digital transactions. Australian Journal of Machine Learning Research & Applications, 2(1), 483–523.
