Group-ID-Based Intelligent Routing: A Precision Routing Framework for Insurance Service Operations
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P120Keywords:
Intelligent Call Routing Insurance, Employer Group ID Routing, Insurance Contact Center Optimization, Plan-Specific Member Service Automation, AI-Driven Routing Healthcare InsuranceAbstract
Insurance contact centers operate under a persistent structural tension between the administrative uniformity that generalist routing assumes and the benefit-plan heterogeneity that employer-sponsored insurance produces. Conventional inquiry-type routing architectures classify incoming member contacts by call category without reference to employer group identity or plan configuration, generating plan-context mismatches that surface as coverage guidance errors, eligibility verification failures, and first-contact resolution breakdowns. This article examines Group-ID-Based Intelligent Routing as a precision-routing architecture that resolves this tension by embedding employer group identifiers directly into routing decisioning logic at the point of member contact. Drawing on peer-reviewed and recognized academic research spanning insurance digitization, intelligent routing systems, healthcare data integrity, contact center automation, and AI-driven service operations, the article addresses the architectural principles of identifier resolution, dynamic queue assignment, infrastructure integration, operational impact, and the strategic pathway toward AI-enhanced routing. The article foregrounds conceptual reasoning, design trade-offs, and boundary conditions across the routing design space, with particular attention to the enrollment data synchronization and legacy system integration constraints that determine real-world implementation viability.
References
1. Tonina Yaneva, "Digital Transformation of Insurance Sector," ResearchGate, 2022. Available: https://www.researchgate.net/publication/358988778_Digital_Transformation_of_Insurance_Sector
2. Ben Atitallah, S., Driss, M., & Ben Ghezala, H. (2023). Revolutionizing disease diagnosis: A microservices-based architecture for privacy-preserving and efficient IoT data analytics using federated learning. arXiv. https://arxiv.org/abs/2308.14017
3. Najmeddine Dhieb et al., "A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement," IEEE Access, 2020. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9046765
4. Lalith Sriram Datla, "Proactive Application Monitoring for Insurance Platforms: How AppDynamics Improved Our Response Times," International Journal of Emerging Research in Engineering and Technology (IJERET), 2023. Available: https://ijeret.org/index.php/ijeret/article/view/134
5. Dheeraj Kumar Dukhiram Pal et al., "Addressing legacy system challenges through EA in healthcare," Data and Metadata (DLABI), 2018. Available: https://dlabi.org/index.php/journal/article/view/185
6. He, W., Zhang, J. Z., Wu, H., Li, W., & Shetty, S. (2022). A unified health information system framework for connecting data, people, devices, and systems. Journal of Global Information Management, 30(1), 1–30. https://doi.org/10.4018/JGIM.305239
7. Zhang, R., Xue, R., & Liu, L. (2021). Security and privacy for healthcare blockchains. IEEE Internet Computing, 25(2), 69–77. https://doi.org/10.1109/MIC.2020.3042245
8. Fathollahi-Fard, A. M., Ahmadi, A., Goodarzian, F., & Cheikhrouhou, N. (2020). A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment. Applied Soft Computing, 93, 106385. https://doi.org/10.1016/j.asoc.2020.106385
9. Wooldridge, M. (2020). Multi-agent systems. AI Magazine, 41(1), 125–128. https://doi.org/10.1609/aimag.v41i1.5197
10. Händler, T. (2023). Balancing autonomy and alignment: A multi-dimensional taxonomy for autonomous LLM-powered multi-agent architectures. arXiv. https://arxiv.org/abs/2310.03659
11. Ahmad N. Gohar et al., "A Patient-Centric Healthcare Framework Reference Architecture for Better Semantic Interoperability Based on Blockchain, Cloud, and IoT," IEEE Access, 2022. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9869824
12. Padala, S. (2023). Intelligent Workforce Management: A Predictive Analytics Approach. American International Journal of Computer Science and Technology, 5(3), 42-47.
13. Padala, S. (2022). Omnichannel AI-Enabled Healthcare Contact Centers: Enabling Seamless Patient Journey Continuity. International Journal of AI, BigData, Computational and Management Studies, 3(1), 133-139.
