A DevOps Framework for Cloud-Native Fraud Detection in Insurance
Keywords:
DevOps Driven Data Engineering, Agentic Artificial Intelligence, Insurance Fraud Detection, Cloud Native Automation, Machine Learning Operations, Continuous Integration Continuous Deployment, Data Governance Frameworks, Risk Sensitive Decision Making, Automated Model Deployment, Predictive Fraud Analytics, Scalable AI Systems, Reproducible Data Pipelines, Secure Data Architectures, Regulatory Compliance In Insurance, Ethical AI Governance, Legacy System Modernization, Autonomous Decision Systems, Operational AI Control, Model Lifecycle Management, Enterprise AI Adoption.Abstract
This study examines how DevOps-driven data engineering can support agentic AI for insurance fraud detection, with an emphasis on cloud-native automation. It argues that data must be centrally secured, accessible, governed, reproducibly high-quality, properly profiled, and efficiently prepared before such systems can be trusted with risk-sensitive decisions. The proposed approach combines CI/CD practices tailored to machine learning with robust data governance, clarifying the underlying technology stack and enabling reliable performance at operational scale.
The work is motivated by mounting regulatory, economic, and ethical pressure on the insurance industry, alongside growing demand for predictive fraud-detection tools and persistent shortcomings in legacy monitoring systems. Automating the development and deployment of machine learning models helps address recurring issues of scalability, reproducibility, and security, while embedding risk assessment directly into the pipeline.
Although organizations increasingly adopt AI to detect fraud, most implementations originate as isolated proof-of-concept efforts that lack the controls needed for dependable, everyday operational use. Agentic AI—systems capable of acting on decisions without direct human intervention—offers a potentially significant competitive advantage, but only when paired with strong governance. Embedding oversight and control mechanisms ensures these autonomous systems operate within acceptable risk boundaries, remain aligned with organizational values, and limit unintended consequences. Current insurance fraud detection solutions largely fail to meet these standards, underscoring the need for the governed, DevOps-based framework this study proposes.
References
1. Subudhi, S., & Panigrahi, S. (2020). Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection. Journal of King Saud University—Computer and Information Sciences, 32(5), 568–575.
2. Geren, Y. (2020). Makine öğrenmesi ile sigorta hasarlarında sahtecilik tespiti. Turkish Studies—Information Technologies and Applied Sciences, 15(2), 195–209.
3. Challa, K., Challa, S. R., Pamisetty, A., Kaulwar, P. K., & Krishna Reddy Koppolu, H. (2025). Transforming Payments: The Role of AI and Big Data in Fraud Alerts, Credit Monitoring, and Secure Transactions. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 1406–1414). IEEE. 2025 IEEE International Conference on Communication Networks and Computing (CNC). https://doi.org/10.1109/cnc68716.2025.11484733
4. Itri, B., Mohamed, Y., Omar, B., & Mohamed, Q. (2020). Empirical oversampling threshold strategy for machine learning performance optimisation in insurance fraud detection. International Journal of Advanced Computer Science and Applications, 11(10).
5. Pranavi, P. S., Sheethal, H. D., Kumar, S. S., Kariappa, S., & Swathi, B. H. (2020). Analysis of vehicle insurance data to detect fraud using machine learning. International Journal for Research in Applied Science & Engineering Technology, 8(7), 2033–2038.
6. Karamitsos, I., Thabit, S., & Apostolopoulos, C. (2020). Applying DevOps practices of continuous automation for machine learning. Information, 11(7), 363.
7. Karlas, B., Interlandi, M., Renggli, C., Wu, W., Zhang, C., Babu, D. M. I., Edwards, J., Lauren, C., Xu, A., & Weimer, M. (2020). Building continuous integration services for machine learning. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
8. Lwakatare, L. E., Crnkovic, I., Rånge, E., & Bosch, J. (2020). From a data science driven process to a continuous delivery process for machine learning systems. In Product-Focused Software Process Improvement (pp. 283–298). Springer.
9. Sivanand, R., Kumar, D. P., Nagabhyru, K. C., Natarajan, E. P., Pamisetty, V., & Kapila, D. (2025, September). IoT and AI for Real-Time Monitoring in Substation Automation. In 2025 International Conference on Computing and Communications (COMPUTINGCON) (pp. 1-5). IEEE.
10. Zhou, Y., Yu, Y., & Ding, B. (2020). Towards MLOps: A case study of ML pipeline platform. In 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). IEEE.
11. Tamburri, D. A. (2020). Sustainable MLOps: Trends and challenges. In 2020 IEEE/ACM International Conference on Software Architecture Companion (ICSA-C). IEEE.
12. Hanafy, M. O. H. A. M. E. D., & Ming, R. (2021). Using machine learning models to compare various resampling methods in predicting insurance fraud. Journal of Theoretical and Applied Information Technology, 99(12), 2819–2833.
13. Pandugula, C., Ganti, V. K. A. T., & Mallesham, G. (2024). Predictive Modeling in Assessing the Efficacy of Precision Medicine Protocols. EDUCATIONAL ADMINISTRATION: THEORY AND PRACTICE Учредители: Green Publication БИБЛИОМЕТРИЧЕСКИЕ ПОКАЗАТЕЛИ: Входит в РИНЦ: на рассмотрении Цитирований в РИНЦ: 0 Входит в ядро РИНЦ: нет Цитирований из ядра РИНЦ: 0 Рецензии: нет данных Процентиль журнала в рейтинге SI: ТЕМАТИЧЕСКИЕ НАПРАВЛЕНИЯ:.
14. Gomes, R., Jin, Z., & Yang, H. (2021). Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance.
15. Choi, J. M., Kim, J. H., & Kim, S. J. (2021). Application of reinforcement learning in detecting fraudulent insurance claims. International Journal of Computer Science & Network Security, 21(9), 125–131.
16. Mäkinen, S. S., Skogström, H., Laaksonen, E., & Mikkonen, T. (2021). Who needs MLOps: What data scientists seek to accomplish and how can MLOps help? In 2021 IEEE/ACM 1st Workshop on AI Engineering—Software Engineering for AI. IEEE.
17. Granlund, T., Kopponen, A., Stirbu, V. A., Myllyaho, L., & Mikkonen, T. (2021). MLOps challenges in multi-organization setup: Experiences from two real-world cases. In 2021 IEEE/ACM 1st Workshop on AI Engineering—Software Engineering for AI. IEEE.
18. Galli, L., et al. (2021). Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata. Machine Learning with Applications, 5, 100074.
19. Vadisetty, R., Nuka, S. T., Kalisetty, S., Pandugula, C., Burugulla, J. K. R., & Annapareddy, V. N. (2025, February). Generative AI for Advanced Recycling Processes in Polyethylene and Polypropylene Manufacturing. In International Ethical Hacking Conference (pp. 269-284). Singapore: Springer Nature Singapore.
20. Van Belle, R., Van Damme, C., Tytgat, H., & De Weerdt, J. (2022). Inductive graph representation learning for fraud detection. Expert Systems with Applications, 193, 116463.
21. 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.
22. Urunkar, A., Khot, A., Bhat, R., & Mudegol, N. L. (2022). Fraud detection and analysis for insurance claim using machine learning. In 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE.
23. Kalra, H., Singh, R., & Kumar, T. S. (2022). Fraud claims detection in insurance using machine learning. Journal of Pharmaceutical Negative Results, 13(Special Issue 3), 327–331.
24. Sathya, M., & Balakumar, B. (2022). Insurance fraud detection using novel machine learning technique. International Journal of Intelligent Systems and Applications in Engineering, 10(3), 374–381.
25. 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.
26. Maguluri, K. K. (2025). Ethical challenges in artificial intelligence. How Artificial Intelligence is Transforming Healthcare IT: Applications in Diagnostics, Treatment Planning, and Patient Monitoring, 132.
27. Hewage, N., & Meedeniya, D. (2022). Machine learning operations: A survey on MLOps tool support. arXiv.
28. Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., & Saif, A. (2022). Financial fraud detection based on machine learning: A systematic literature review. Applied Sciences, 12(19), 9637.
29. Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11, 31866–31879.
30. 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.
31. 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.
32. Hancock, J. T., Bauder, R. A., Wang, H., & Khoshgoftaar, T. M. (2023). Explainable machine learning models for Medicare fraud detection. Journal of Big Data, 10, 154.
33. Kaya, H. (2024). Riskified fraud detection using machine learning: Insurance claims. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, 5(1), 39–56.
34. Vorobyev, I. (2024). Fraud risk assessment in car insurance using claims graph features in machine learning. Expert Systems with Applications, 251, 124109.
35. Doulat, A. A., Ayo-Bali, O. E., & Shaik, S. (2025). Fraud detection in insurance claims using supervised machine learning models. In 2025 7th International Conference on Smart Applications, Communications and Networking (SmartNets 2025). IEEE.
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