Cloud-Native DevOps for GenAI-Powered Insurance Risk & Fraud Intelligence
Keywords:
Cloud-Native DevOps for AI, Insurance Risk Intelligence Systems, Fraud Detection Analytics, Generative Artificial Intelligence in Insurance, AI CI/CD Pipelines, MLOps for Regulated Industries, Containerization and Orchestration Technologies, Multi-Cloud Portability, Model Versioning and Rollback Controls, Data Versioning and Validation, Observability and Safety Gates, Canary Releases in AI Deployment, Compliance-by-Design Architectures, Operational Risk Controls in AI Systems, Scalable Insurance AI Workloads.Abstract
Cloud-native DevOps for AI pipelines in insurance risk intelligence and fraud detection promotes replicability and composability across use cases. Support for scalable workloads addresses high costs, disk and memory limits, and specific cost and time-saving demands. Cloud-native principles—composability, elasticity, portability, observability, security, formal compliance, and data governance—are applied to underpin a generative AI-oriented insurance architecture. The DevOps component tailors continuous integration and continuous delivery (CI/CD) practices to be responsible, reproducible, reliable, and safe. Infrastructure depends on containerization and orchestration technologies to fully contemplate reproducibility, observability, isolation, and multi-cloud portability. AI workload provisioning adheres to a service model. For safe operation, specialized AI CI/CD pipelines provide model versioning and rollback, data versioning and validation, evaluation, performance metrics and safety gates for canary releases, and go/no-go decisions based on business risk appetite. Their design highlights domain-specific operational risk controls.
Different generative AI applications in insurance cover a wide spectrum of functionalities, underlining the fundamental basis for the creation of these models and the capability of these applications to adapt to different environments. Nevertheless, models must be handled with care in order to mitigate potential ethical, social, legal, regulatory, and business-related issues. Risk management and operational risk must be part of the implementation which, when properly completed, can provide a safe way to integrate these types of applications. Several risk intelligence use cases point to specific implementations demonstrated during the CapGemini FBDX programme and depict high-priority paths in the integration of such approaches, illustrating data origin and target indicators.
References
1. Gomes, C., Jin, Z., & Yang, H. (2021). Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance, 88(3), 591–624.
2. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2021). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 34.
3. Balamurugan, J., Bhuvaneswari, M., Aitha, A. R., Nagaraju, S., Viswanathan, R., & Dhasarathan, N. (2025, November). Explanatory Transformer-Based Sequential Recommendation: Assessing BERTRec with SASRec for Customer Behaviour Prediction. In 2025 International Conference on Emerging Engineering Technologies and Applications (IC-EETA) (pp. 470-475). IEEE.
4. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35.
5. Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., & Shams, T. (2022). Insurance fraud detection: Evidence from artificial intelligence and machine learning. Research in International Business and Finance, 62, 101744.
6. Amistapuram, K., Pandiri, L., Raju, V. R., Paleti, S., Singireddy, S., & Sheelam, G. K. (2025). AI-Based Cloud Infrastructure and MLOps Frameworks for Scalable Data Engineering Across Banking and Insurance. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 186–192). IEEE. 2025 IEEE International Conference on Communication Networks and Computing (CNC). https://doi.org/10.1109/cnc68716.2025.11484532
7. Xia, H., Zhou, Y., & Zhang, Z. (2022). Auto insurance fraud identification based on a CNN-LSTM fusion deep learning model. International Journal of Ad Hoc and Ubiquitous Computing, 39(1–2), 37–45.
8. Hewage, N., & Meedeniya, D. (2022). Machine learning operations: A survey on MLOps tool support. arXiv preprint arXiv:2202.10169.
9. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., … Liang, P. (2022). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
10. Inala, R. (2025). A Unified Framework for Agentic AI and Data Products: Enhancing Cloud, Big Data, and Machine Learning in Supply Chain, Insurance, Retail, and Manufacturing. EKSPLORIUM-BULETIN PUSAT TEKNOLOGI BAHAN GALIAN NUKLIR, 46(1), 1614-1628.
11. Schick, T., Dwivedi-Yu, J., Dessi, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36.
12. Park, J. S., O’Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, 1–22.
13. Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11, 31866–31879.
14. Goel, A. V., Kumari, P., Vaghela, K., Nagabhyru, K. C., Karichalil, R. A., Salman, S. A., & Brahmane, P. (2025). STRATEGIC CHANGE MANAGEMENT IN THE ERA OF DIGITAL DISRUPTION: AN INTERDISCIPLINARY STUDY ON ORGANISATIONAL ADAPTABILITY AND INNOVATION CULTURE. Scientific Culture, 11(4), 236.
15. Ruf, P., Madan, M., Reich, C., & Olex, A. (2023). Demystifying MLOps: A systematic literature review. IEEE Access, 11, 110111–110132.
16. Settipalli, L., & Gangadharan, G. R. (2023). WMTDBC: An unsupervised multivariate analysis model for fraud detection in health insurance claims. Expert Systems with Applications, 215, 119259.
17. Debener, J., Heinke, V., & Kriebel, J. (2023). Detecting insurance fraud using supervised and unsupervised machine learning. Journal of Risk and Insurance, 90(3), 743–768.
18. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
19. Maiano, L., Montuschi, A., Caserio, M., Ferri, E., Kieffer, F., Germanò, C., Baiocco, L., Ricciardi Celsi, L., Amerini, I., & Anagnostopoulos, A. (2023). A deep-learning-based antifraud system for car-insurance claims. Expert Systems with Applications, 231, 120644.
20. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. International Conference on Learning Representations.
21. Isukapatla, M., Davuluri, P. N., Satya, G. S., & Prakash, P. R. (2026, April). An Attention-Enhanced YOLO Framework with IMU Confidence for Road Surface Defect Analysis. In 2026 2nd International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-8). IEEE.
22. Liu, Y., Deng, G., Li, Y., Wang, K., Wang, Z., Wang, X., Zhang, T., Liu, Y., Wang, H., Zheng, Y., & Liu, Y. (2023). Prompt injection attack against LLM-integrated applications. arXiv preprint arXiv:2306.05499.
23. Schreiner, M., & colleagues. (2023). The pipeline for the continuous development of artificial intelligence models—Current state of research and practice. Journal of Systems and Software, 199, 111615.
24. Yandamuri, U. S., Loganathan, R., Davuluri, P. N., Rani, P. S., Kolla, S. H., & Nagubandi, A. R. (2026, June). Adaptive Intelligence Networks for Humancentered Enterprise Automation and Governance. In 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS) (pp. 678-683). IEEE.
25. Huang, X., Ruan, W., Huang, W., Jin, G., Dong, Y., Wu, C., Bensalem, S., Mu, R., Qi, Y., Zhao, X., Cai, K., Zhang, Y., Wu, S., Xu, P., Wu, D., Freitas, A., & Mustafa, M. A. (2024). A survey of safety and trustworthiness of large language models through the lens of verification and validation. Artificial Intelligence Review, 57, Article 175.
26. Wang, Y., Wang, M., Manzoor, M. A., Liu, F., Georgiev, G. N., Das, R. J., & Nakov, P. (2024). Factuality of large language models: A survey. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 19519–19529.
27. Priyanka, R. P., Annapareddy, V. N., Yenugu, B. C., Nagabhyru, K. C., & Kapila, D. (2025, September). Optimization of Battery Management Systems Using Machine Learning. In 2025 International Conference on Computing and Communications (COMPUTINGCON) (pp. 1-6). IEEE.
28. Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., Ye, W., Zhang, Y., Chang, Y., Yu, P. S., Yang, Q., & Xie, X. (2024). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15(3), Article 39.
29. Kumar, P. (2024). Large language models (LLMs): Survey, technical frameworks, and future challenges. Artificial Intelligence Review, 57, Article 260.
30. Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N. V., Wiest, O., & Zhang, X. (2024). Large language model based multi-agents: A survey of progress and challenges. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 8048–8057.
31. Li, X., Wang, S., Zeng, S., Wu, Y., & Yang, Y. (2024). A survey on LLM-based multi-agent systems: Workflow, infrastructure, and challenges. Journal of Computer Science and Technology, 39, 1–25.
32. Lebcir, I., Mageswari, S. D., Bhosale, Y. H., Nagubandi, A. R., & Mahabooba, M. (2025). Agile Strategic Management in the Age of Disruption: Leveraging AI and Data Analytics for Competitive Advantage. Advances in Consumer Research, 2(6), 2581.
33. Vorobyev, I. (2024). Fraud risk assessment in car insurance using claims graph features in machine learning. Expert Systems with Applications, 251, 124109.
34. Schrijver, G., Sarmah, D. K., & El-hajj, M. (2024). Automobile insurance fraud detection using data mining: A systematic literature review. Intelligent Systems with Applications, 21, 200340.
35. Khalil, A. A., Liu, Z., Fathalla, A., Ali, A., & Salah, A. (2024). Machine learning based method for insurance fraud detection on class imbalance datasets with missing values. IEEE Access, 12, 155451–155468.
36. Ganesh, S. K., Subbareddy, K., Gopi, A., Davuluri, P. N., Mannar, B. R., & Karnawat, A. T. (2026, June). Fraudulent Credit Card Transaction Detection Using a Hybrid Ensemble-Anomaly Detection Framework. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
37. Lin, Z., Guan, S., Zhang, W., Zhang, H., Li, Y., & Zhang, H. (2024). Towards trustworthy LLMs: A review on debiasing and dehallucinating in large language models. Artificial Intelligence Review, 57, Article 243.
38. Kumar, P., Ekin, T., Park, C., Markey, M. K., Barner, J. C., & Rascati, K. (2024). Using a Bayesian belief network to detect healthcare fraud. Expert Systems with Applications, 238, 122241.
39. Kumar, P. A., & Sountharrajan, S. (2025). Insurance claims estimation and fraud detection with optimized deep learning techniques. Scientific Reports, 15, Article 27296.
40. Garapati, R. S. (2025). Real-Time Monitoring and AI-Based Control of Industrial Robots Using Cloud-Hosted Web Applications. Available at SSRN 5612491.
41. Yankol-Schalck, M. (2026). Auto insurance fraud detection: Machine learning and deep learning applications. Journal of Risk and Insurance, 93(2), 534–568.
