AI-Driven DevOps Framework for Financial Inclusion, Secure Healthcare and Insurance Systems Using BERT Intelligence and SDN
DOI:
https://doi.org/10.15662/IJARCST.2025.0805017Keywords:
AI-Driven DevOps, BERT-Based Intelligence, Financial Inclusion, Proactive Incident Management, Secure Healthcare Systems, Insurance Technology (InsurTech), Explainable AI, Sustainable Cloud Operations, Federated LearningAbstract
The integration of Artificial Intelligence (AI) into DevOps pipelines is transforming digital service delivery, enabling intelligent automation, proactive monitoring, and secure system operations across critical industries. This paper proposes an AI-driven DevOps framework that leverages BERT-based intelligence to enhance financial inclusion, incident management, and cybersecurity in healthcare and insurance systems. The framework combines contextual natural language understanding, anomaly prediction, and semantic event correlation to improve decision-making in real time.
BERT-based models are deployed within CI/CD environments to process log data, user feedback, and policy documentation, enabling automated root-cause analysis and adaptive risk mitigation. In financial inclusion, explainable predictive analytics powered by fine-tuned BERT embeddings support fair credit assessment and dynamic microinsurance recommendations for underbanked populations. For healthcare and insurance domains, the architecture integrates secure NLP-driven knowledge graphs and federated data governance mechanisms to detect fraud, ensure policy compliance, and protect patient information under HIPAA and GDPR standards.
The system’s cloud-native implementation employs container orchestration, distributed model inference, and green DevOps practices to ensure scalability, resilience, and sustainability. Experimental evaluation indicates a 33% reduction in incident resolution time, a 27% increase in anomaly detection accuracy, and improved data security metrics compared with traditional monitoring systems. The proposed BERT-augmented DevOps framework establishes a new paradigm for intelligent, secure, and inclusive digital transformation across financial and healthcare ecosystems.
References
1. Khalid, N. (2023). Privacy-preserving artificial intelligence in healthcare. The Lancet Digital Health, 5(11), e759–e768. https://doi.org/10.1016/S2589-7500(23)00131-6
2. Balaji, P. C., & Sugumar, R. (2025, June). Multi-level thresholding of RGB images using Mayfly algorithm comparison with Bat algorithm. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020180). AIP Publishing LLC.
3. Lanka, S. (2025). AI-driven workflow transformation in clinical practice: Evaluating the effectiveness of Dragon Copilot. International Journal of Health Informatics, 12(3), 245–259. https://doi.org/10.34218/IJHI_12_03_021
4. Narapareddy, V. S. R., &Yerramilli, S. K. (2024a). Devops Compliance-as-Code. Universal Library of Engineering Technology., 01(02), 47–54. https://doi.org/10.70315/uloap.ulete. 2024.0102008
5. Liu, J., Li, X., Ye, L., Zhang, H., Du, X., & Guizani, M. (2018). BPDS: A blockchain-based privacy-preserving data sharing for electronic medical records. arXiv preprint arXiv:1811.03223. https://arxiv.org/abs/1811.03223
6. Mula, K. (2025). Financial Inclusion through Digital Payments: How Technology is Bridging the Gap. Journal of Computer Science and Technology Studies, 7(2), 447-457. file:///C:/Users/Admin/Downloads/Paper+46+(2025.7.2)+Financial+Inclusion+through+Digital+Payments.pdf
7. Kondra, S., Raghavan, V., & kumar Adari, V. (2025). Beyond Text: Exploring Multimodal BERT Models. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 11764-11769.
8. Lin, T. (2024). The role of generative AI in proactive incident management: Transforming infrastructure operations. International Journal of Innovative Research in Science, Engineering and Technology, 13(12), Article — . https://doi.org/10.15680/IJIRSET.2024.1312014
9. Joseph, Jimmy. (2024). AI-Driven Synthetic Biology and Drug Manufacturing Optimization. International Journal of Innovative Research in Computer and Communication Engineering. 12. 1138. 10.15680/IJIRCCE.2024.1202069. https://www.researchgate.net/publication/394614673_AI Driven_Synthetic_Biology_and_Drug_Manufacturing_Optimization
10. Azmi, S. K. (2021). Delaunay Triangulation for Dynamic Firewall Rule Optimization in Software-Defined Networks. Well Testing Journal, 30(1), 155-169.
11. Gandhi, S. T. (2024). Enhancing Software Security with AI-Powered SDKs: A Framework for Proactive Threat Mitigation. International Journal of Computer Technology and Electronics Communication, 7(2), 8507-8514.
12. Jonnagaddala, J., & Ponnusamy, V. (2025). Privacy-preserving strategies for electronic health records in the age of generative AI. npj Digital Medicine, 8(1), 1–7. https://doi.org/10.1038/s41746-025-01429-0
13. Alruwaill, M. N., Mohanty, S. P., & Kougianos, E. (2025). hChain 4.0: A secure and scalable permissioned blockchain for EHR management in smart healthcare. arXiv preprint arXiv:2505.13861. https://arxiv.org/abs/2505.13861
14. Lee, S. (2021). Prospect of artificial intelligence based on electronic health records. Journal of Healthcare Engineering, 2021, 1–10. https://doi.org/10.1155/2021/8473961
15. Adari, V. K. (2024). The Path to Seamless Healthcare Data Exchange: Analysis of Two Leading Interoperability Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11472-11480.
16. Konda, S. K. (2023). The role of AI in modernizing building automation retrofits: A case-based perspective. International Journal of Artificial Intelligence & Machine Learning, 2(1), 222–234. https://doi.org/10.34218/IJAIML_02_01_020
17. Sajja, J. W., Komarina, G. B., & Choppa, N. K. R. (2025). The Convergence of Financial Efficiency and Sustainability in Enterprise Cloud Management. Journal of Computer Science and Technology Studies, 7(4), 964-992.
18. Raju, L. H. V., & Sugumar, R. (2025, June). Improving jaccard and dice during cancerous skin segmentation with UNet approach compared to SegNet. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020271). AIP Publishing LLC.
19. Liaw, S. T., & Tan, J. (2020). Ethical use of electronic health record data and artificial intelligence. Journal of Medical Internet Research, 22(10), e17970. https://doi.org/10.2196/17970


