Multi-Modal Deep Learning for Life Insurance: Anomaly Detection in Robotics, Automation, and IoT with Multi-Team QA
Main Article Content
Abstract
This paper presents a multi-modal deep learning framework for life insurance operations, integrating anomaly detection across robotics, automation, and IoT systems while optimizing multi-team quality assurance (QA). Modern life insurance ecosystems involve complex workflows, large volumes of sensitive data, and automated processes that require precise monitoring to prevent errors, fraud, and operational inefficiencies. The proposed framework leverages multi-modal deep learning models to analyze heterogeneous data from robotic process automation, IoT sensors, and operational logs, detecting anomalies in real time. Multi-team QA coordination is enhanced through AI-driven insights, ensuring consistency, regulatory compliance, and streamlined workflows. Experimental results demonstrate improved anomaly detection accuracy, reduced operational risk, and enhanced team productivity, highlighting the potential of multi-modal deep learning to create secure, efficient, and intelligent life insurance operations.
Article Details
Section
How to Cite
References
1. Soriano, G. P., Yasuhara, Y., Ito, H., Matsumoto, K., Osaka, K., Kai, Y., Locsin, R., Schoenhofer, S., & Tanioka, T. (2022). Robots and robotics in nursing. Healthcare, 10(8), 1571. MDPI
2. Eze, C. (2023). Internet of Things Meets Robotics: A Survey of Cloud based Robots. arXiv preprint arXiv:2306.02586. arXiv
3. Balaji, P. C., & Sugumar, R. (2025, June). Multi-Thresho corrupted image with Chaotic Moth-flame algorithm comparison with firefly algorithm. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020179). AIP Publishing LLC.
4. Khaksar, W., Saplacan, D., Bygrave, L. A., & Torresen, J. (2023). Robotics in Elderly Healthcare: A Review of 20 Recent Research Projects. arXiv preprint arXiv:2302.04478. arXiv
5. 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.
6. Johnson, T., & Woodward, K. (2025). Enviro IoT: Calibrating Low Cost Environmental Sensors in Urban Settings. arXiv preprint arXiv:2502.07596. arXiv
7. Shekhar, P. C. (2024). Chaos Testing: A Proactive Framework for System Resilience in Distributed Architectures.
8. “Robotics in Healthcare: a Survey.” (2023). SN Computer Science. SpringerLink
9. Peddamukkula, P. K. (2024). The Impact of AI-Driven Automated Underwriting on the Life Insurance Industry. International Journal of Computer Technology and Electronics Communication, 7(5), 9437-9446.
10. Automation in Claims Processing. (n.d.). EIS. Retrieved from EIS website. EIS
11. Reddy, B. V. S., & Sugumar, R. (2025, June). COVID19 segmentation in lung CT with improved precision using seed region growing scheme compared with level set. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020154). AIP Publishing LLC.
12. Empeek. (n.d.). Automation in Insurance Claims Processing & Impact on the Future. Empeek
13. “Artificial Intelligence With Robotics in Healthcare: A Narrative Review of Its Viability in India.” (2022). Frontiers / PubMed. PubMed
14. HIMSS. (2021). Robotics in Healthcare. Online Journal of Nursing Informatics. HIMSS
15. Time. (2024). Robots Created to Help Patients in Hospitals Pass Testing Phase. TIME
16. Joseph, J. (2023). DiffusionClaims–PHI-Safe Synthetic Claims for Robust Anomaly Detection. International Journal of Computer Technology and Electronics Communication, 6(3), 6958-6973.
17. Policybazaar for Business introduces ClaimSetu: India’s first AI led claims insights and scoring engine for group health insurance claims. Economic Times. (2025). The Economic Times
18. Gandhi, S. T. (2024). Fusion of LiDAR and HDR Imaging in Autonomous Vehicles: A Multi-Modal Deep Learning Approach for Safer Navigation. International Journal of Humanities and Information Technology, 6(03), 6-18.
19. Star Health to Outsource Claims Processing to Medi Assist’s AI Platform. Times of India. (2025). The Times of India
20. Sethupathy, U. K. A. (2024). Fraud Detection Mechanisms in Virtual Payment Systems. International Journal of Computer Technology and Electronics Communication, 7(2), 8504-8515. Komarina, G. B. (2024). Transforming Enterprise Decision-Making Through SAP S/4HANA Embedded Analytics Capabilities. Journal ID, 9471, 1297.
21. Poovaiah, S. A. D. (2022). Benchmarking provable resilience in convolutional neural networks: A study with Beta-CROWN and ERAN.
22. Affective Robotics for Wellbeing: A Scoping Review. (2023). arXiv preprint arXiv:2304.01902. arXiv
23. “Robotics Applications in the Hospital Domain: A Literature Review.” (2020). Applied System Innovation. Directory of Open Access Journals
24. “Robotics and Artificial Intelligence in Healthcare during COVID 19 Pandemic: A Systematic Review.” (2021). Elsevier / PubMed.