Cloud Native Resilient Architectures for Open Banking APIs with Real-Time Fraud Detection and Transparent Encryption Mechanisms
Abstract
The financial services sector is rapidly evolving toward open banking models, driven by regulatory initiatives such as PSD2 in Europe and the demand for seamless digital services. Open banking APIs enable third-party providers to access banking services, driving innovation in payments, lending, and financial management. However, these integrations expose sensitive financial data and increase the risk of cyberattacks, fraud, and regulatory non-compliance. This research proposes a cloud-native resilient architecture for open banking APIs that integrates real-time fraud detection, transparent encryption mechanisms, and automated operational resilience to ensure secure, scalable, and compliant financial services delivery.
The proposed architecture leverages containerized microservices deployed on Kubernetes clusters within hyperscale cloud platforms, enabling elastic scaling, high availability, and fault tolerance. Real-time fraud detection is achieved using machine learning models trained on historical transaction data, user behavior analytics, and anomaly detection techniques. These models can identify suspicious activities, such as unauthorized fund transfers, abnormal login patterns, and transaction anomalies, minimizing the risk of financial losses. The architecture supports continuous learning, allowing fraud detection models to adapt to evolving threat landscapes without disrupting services.
Transparent encryption mechanisms are implemented at both data-at-rest and data-in-transit layers, employing envelope encryption, key rotation, and tokenization strategies. This ensures that sensitive banking information is protected end-to-end without affecting operational efficiency. API gateways and policy-based access controls enforce fine-grained authorization and monitoring, reducing attack surfaces while maintaining interoperability with third-party applications.
Automated DevOps pipelines, including continuous integration, continuous deployment, and infrastructure-as-code (IaC), streamline updates, reduce downtime, and ensure regulatory compliance. Observability and monitoring frameworks provide real-time insights into API performance, system health, and security posture, supporting rapid incident response and proactive risk mitigation.
The proposed cloud-native architecture enhances scalability, resilience, and security for open banking APIs while enabling compliance with financial regulations such as PSD2, GDPR, and PCI-DSS. By integrating real-time fraud detection and transparent encryption into a holistic DevOps-driven platform, financial institutions can safely leverage open banking innovations while protecting sensitive data and maintaining customer trust. This research contributes to the development of intelligent, secure, and resilient open banking ecosystems capable of supporting the next generation of digital financial services.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 8 No. 4 (2025): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
14578-14588 |
Published |
August 6, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Carsten Griwodz (%2025). Cloud Native Resilient Architectures for Open Banking APIs with Real-Time Fraud Detection and Transparent Encryption Mechanisms. International Journal of Science, Research and Technology , Vol. 8 No. 4 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14578-14588. https://doi.org/10.15662/IJSRAT.2025.0804003 |
References
2. Rao, N. S., Shanmugapriya, G., Vinod, S., & Mallick, S. P. (2023, March). Detecting human behavior from a silhouette using convolutional neural networks. In 2023 Second International Conference on Integrated Circuits and Communication Systems (ICEARS) (pp. 943–948). IEEE.
3. Ramidi, M. (2023). Implementing privacy-focused data sharing frameworks for mobile healthcare communication. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8746–8757.
4. Mangukiya, M. (2025). Advanced testing and validation frameworks for high-reliability multi-board electronic systems. International Journal of Computational and Experimental Science and Engineering, 11(4).
5. Itoo, S., Khan, A. A., Ahmad, M., & Idrisi, M. J. (2023). A secure and privacy-preserving lightweight authentication and key exchange algorithm for smart agriculture monitoring system. IEEE Access, 11, 56875–56890.
6. Genne, S. (2023). Optimizing user experience in high-traffic financial web applications using analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 7231–7241.
7. Kamadi, S. (n.d.). Zero trust architecture implementation in hybrid financial technology ecosystems: A comprehensive framework for regulated environments. Retrieved from ResearchGate.
8. Devi, C., Vunnam, N., & Jeyaraman, J. (2022). HyperLogLog-based compliance coverage estimation for distributed datasets. Essex Journal of AI Ethics and Responsible Innovation, 2, 495–530.
9. Gaddapuri, N. S. (2024). AI BASED CLOUD COMPUTATION METHOD AND PROCESS DEVELOPMENT. Power System Protection and Control, 52(2), 38-50.
10. Ponnoju, S. C., & Venkatachalam, D. (2024). Containerization efficiency in financial services: Performance enhancement using Kubernetes (EKS) and CI/CD pipelines with Starling. Essex Journal of AI Ethics and Responsible Innovation, 4, 129–168.
11. Vishwarup, S., et al. (2020). Automatic person count indication system using IoT in a hotel infrastructure. In 2020 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–4). IEEE.
12. Gurajapu, A., & Garimella, V. (2025). Secure service-mesh implementations: Mitigating lateral-movement risks in container-based telecom apps. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 11812–11816.
13. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
14. Mudunuri, P. R. (2024). Designing high-availability automation architectures for mission-critical research systems. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13852–13864.
15. Paul, D., Sudharsanam, S. R., & Surampudi, Y. (2021). Implementing continuous integration and continuous deployment pipelines in hybrid cloud environments: Challenges and solutions. Journal of Science & Technology, 2(1), 275–318.
16. Akhtaruzzaman, K., MdAbulKalam, A., Mohammad Kabir, H., & KM, Z. (2024). Driving US Business Growth with AI-Driven Intelligent Automation: Building Decision-Making Infrastructure to Improve Productivity and Reduce Inefficiencies. American Journal of Engineering, Mechanics and Architecture, 2(11), 171-198. http://eprints.umsida.ac.id/16412/1/171-198%2BDriving%2BU.S.%2BBusiness%2BGrowth%2Bwith%2BAI-Driven%2BIntelligent%2BAutomation.pdf
17. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
18. Gopinathan, V. R. (2024). Secure explainable AI on Databricks–SAP cloud for risk-sensitive healthcare analytics and swarm-based QoS control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8452–8459.
19. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
20. Ananth, S., Kalpana, A. M., & Vijayarajeswari, R. (2020). A dynamic technique to enhance quality of service in software-defined network-based wireless sensor network (DTEQT) using machine learning. International Journal of Wavelets, Multiresolution and Information Processing, 18(01), 1941020.
21. Mulla, F. A. (2024). The mobile revolution during COVID-19: A technical analysis of application evolution. International Journal for Multidisciplinary Research (IJFMR), 6(6), Article 33494.
22. Adepu, R. (2025). Green cloud infrastructure: Energy-aware scheduling and sustainable data center design. International Journal of Computer Technology and Electronics Communication, 8(4), 210–226.
23. Sarabu, V. B. (2018). A framework-driven approach to data validation and reconciliation for operational accuracy. International Journal of Research and Applied Innovations, 1(1), 2130-2140.
24. Kotla, M. R. T. (2023). AI in consumer digital banking: Enabling smart personalization and fraud detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 262–276.
25. Nerella, A., Badri, P., Kandula, S. T. R., Surasani, V. R., Muthukamatchi, P. K., & Jain, A. (2025, August). Neurosymbolic AI for IoT Security: A Knowledge-Guided Framework for Real-Time IoT Anomaly Detection and Response. In 2025 Seventeenth International Conference on Contemporary Computing (IC3) (pp. 1-5). IEEE.
26. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
27. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
28. Shewale, V. (2024). Ransomware Resilience for Pipeline Operators. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7863-7868.
29. Parasa, M. (2024). Intelligent compliance automation in SAP SuccessFactors: AI monitoring for global labor law adherence. International Research Journal of Engineering & Applied Sciences, 12(3). https://doi.org/10.55083/irjeas.2024.v12i03006
30. Namdeo, A. (2024). Autonomous data quality management via ML in cloud warehouses. International Journal of Humanities and Information Technology, 6(04), 124-131.
31. Panyala, V. R. (2022). Integrating AI-driven autoscaling mechanisms in Kubernetes-based microservices architectures. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(4), 9–21.
32. Adepu, G. (2022). Graph AI–Driven Environmental Intelligence Platforms for Predictive Regulatory Risk Assessment. International Journal of Computer Technology and Electronics Communication, 5(5), 5776-5780.
33. Narayanan, S. (2024). Third-party AI vendor risk: Developing assessment frameworks for machine learning service providers. International Journal of Computer Science and Engineering and Information Technology, 10(4), 1133–1142. https://philarchive.org/archive/NARTAV
34. Kunadi, S. K. (2024). From raw data to revenue intelligence: Architecting GTM data platforms for business impact. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12414.
35. Kondisetty, K., Mohammed, A. S., & Muthusamy, P. (2024). Omni-channel customer onboarding with NLP-powered document intelligence. Journal of Artificial Intelligence & Machine Learning Studies, 8, 124–157.
36. Inbavalli, M., & Arasu, T. (2015). Efficient analysis of frequent item set association rule mining methods. International Journal of Scientific & Engineering Research, 6(4).
37. Bairi, A. R., Thangavelu, K., & Keezhadath, A. A. (2024). Quantum computing in test automation: Optimizing parallel execution with quantum annealing in D-Wave systems. Journal of Artificial Intelligence General Science (JAIGS), 5(1), 536–545.
38. Prasanna, D., & Manishvarma, R. (2025, February). Skin cancer detection using image classification in deep learning. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–8). IEEE.