AI and Deep Learning-Enabled Life Insurance Ecosystem: Robust Anomaly Detection, Automation, and Optimized Multi-Team QA
Main Article Content
Abstract
This paper presents an AI and deep learning-enabled life insurance ecosystem designed to enhance anomaly detection, process automation, and multi-team quality assurance (QA) optimization. Life insurance operations involve complex workflows, large volumes of sensitive policy data, and multi-department coordination, which pose challenges for accuracy, efficiency, and compliance. The proposed framework integrates deep learning models with AI-driven analytics to detect anomalies in claims, underwriting, and policy management processes, enabling proactive mitigation of errors and fraud. Automated workflows streamline repetitive tasks across teams, while optimized QA mechanisms ensure consistency, regulatory compliance, and improved service quality. Experimental evaluations demonstrate significant improvements in anomaly detection accuracy, operational efficiency, and cross-team coordination, highlighting the potential of AI and deep learning to transform life insurance ecosystems into secure, agile, and data-driven operations.
Article Details
Section
How to Cite
References
1. Huang, J., He, R., Khan, D. Z., Mazomenos, E., Stoyanov, D., Marcus, H. J., Clarkson, M. J., & Islam, M. (2025). SurgicalVLM Agent: Towards an interactive AI co pilot for pituitary surgery. arXiv preprint arXiv:2503.09474. arXiv
2. Oquendo Torres, F. A., & Segovia Vargas, M. J. (2024). Sustainability risk in insurance companies: A machine learning analysis. Global Policy, 15(S7), 47 64.
3. Reddy, B. T. K., & Sugumar, R. (2025, June). Effective forest fire detection by UAV image using Resnet 50 compared over Google Net. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020274). AIP Publishing LLC.
4. Aryia Dattamajumdar, et al. (2021). An early warning AI powered portable system to reduce workload and inspect environmental damage after natural disasters. arXiv preprint arXiv:2104.00876. arXiv
5. SatHealth: A multimodal public health dataset with satellite based environmental factors. (2025). arXiv preprint arXiv:2506.13842. arXiv
6. “IoT enabled Environmental Toxicology for Air Pollution Monitoring using AI techniques (ETAPM AIT).” (2022). Environmental Toxicology and Pollution Management. PubMed
7. From Diagnosis to Precision Surgery: The Transformative Role of Artificial Intelligence in Urologic Imaging. (2023). Journal of Urologic Imaging. PubMed
8. Poovaiah, S. A. D. (2022). Benchmarking provable resilience in convolutional neural networks: A study with Beta-CROWN and ERAN.
9. Joseph, J. (2023). DiffusionClaims–PHI-Safe Synthetic Claims for Robust Anomaly Detection. International Journal of Computer Technology and Electronics Communication, 6(3), 6958-6973.
10. Enhancing Surgical Precision: A Systematic Review of Wearable Medical Devices for Assisted Surgery. (2025). Computers in Biology and Medicine, 196, 110752. PubMed
11. “Robotics and artificial intelligence in surgery: Precision, safety, and innovation.” Maguluri, K. K. (2024). In Deep Science Publishing. Deep Science Research
12. “Artificial intelligence and environmental health.” NEHA. (2024). National Environmental Health Association. neha.org.
13. 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.-ctece may 2025
14. Peddamukkula, P. K. (2024). Artificial Intelligence in Life Expectancy Prediction: A Paradigm Shift for Annuity Pricing and Risk Management. International Journal of Computer Technology and Electronics Communication, 7(5), 9447-9459.
15. Komarina, G. B. ENABLING REAL-TIME BUSINESS INTELLIGENCE INSIGHTS VIA SAP BW/4HANA AND CLOUD BI INTEGRATION.
16. Karanjkar, R., & Karanjkar, D. (2024). Optimizing Quality Assurance Resource Allocation in Multi Team Software Development Environments. International Journal of Technology, Management and Humanities, 10(04), 49-59.
17. GlobalData. (2024). AI revolutionizes general surgery with unprecedented precision and patient outcomes. GlobalData report. GlobalDataGandhi, S. T. (2023). RAG-Driven Cybersecurity Intelligence: Leveraging Semantic Search for Improved Threat Detection. International Journal of Research and Applied Innovations, 6(3), 8889-8897.
18. Sethupathy, U. K. A. (2024). Zero-Trust Payment Infrastructures: A GenAI-Driven Threat Detection Mesh for Digital Wallet Ecosystems. International Journal of Research and Applied Innovations, 7(1), 10109-10119.
19. P. Chatterjee, “AI-Powered Payment Gateways : Accelerating Transactions and Fortifying Security in RealTime Financial Systems,” Int. J. Sci. Res. Sci. Technol., 2023.
20. Definition Health & University Hospitals Sussex NHS Foundation Trust. (2025). Advancing surgical precision with AI powered risk stratification tool (SURGIA). Surgery International. Surgery International
21. Peddamukkula, P. K. (2024). The Role and Types of Automation in the Life Insurance Industry. International Journal of Computer Technology and Electronics Communication, 7(5), 9426-9436.
22. Environmental Assessment Based on Health Information Using Artificial Intelligence. (2021). In Advances in Artificial Intelligence, Computation, and Data Science, Springer. SpringerLink
23. Gandhi, S. T. (2025). AI-Driven Smart Contract Security: A Deep Learning Approach to Vulnerability Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(1), 11540-11547.
24. Asensus Surgical. AugmentOR Portal: data driven analytics from surgical robots and cameras. Time magazine article. TIME
25. Shekhar, P. C. (2024). From Automation to Intelligence: Revolutionizing Microservices and API Testing with AI.
26. 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.
27. GUPTA, A. B., et al. (2023). "Smart Defense: AI-Powered Adaptive IDs for Real-Time Zero-Day Threat Mitigation."