THE TECHNICAL IMPLEMENTATION OF AGENTIC AI IN MODERN INSURANCE OPERATIONS
DOI:
https://doi.org/10.34218/IJCET_16_01_147Keywords:
Artificial Intelligence In Insurance, Claims Processing Automation, Fraud Detection Mechanisms, Human-in-the-Loop Architecture, Risk Assessment FrameworkAbstract
This technical article explores the transformative impact of Agentic Artificial Intelligence (AAI) systems within the insurance industry, focusing on key operational domains including claims processing, underwriting, and fraud detection. The article explores how modern insurance providers are leveraging advanced technologies such as deep learning, computer vision, and natural language processing to optimize their operations. The implementation of these AI-driven systems has revolutionized traditional workflows, from automated damage assessment in claims processing to sophisticated risk evaluation in underwriting, while maintaining robust security and compliance standards. The article also highlights the critical role of human-in-the-loop architectures and bias mitigation frameworks in ensuring accurate and equitable insurance operations. Through comprehensive analysis of system architectures, implementation strategies, and performance metrics, this article provides insights into how AAI systems are reshaping the insurance landscape while addressing challenges related to system integration, security, and quality assurance.
References
Naman Kumar, et al., "Artificial Intelligence in Insurance Sector," International Conference on Multi- Disciplinary Research and Innovation(IARDO Rising Star Awards-2019). [Online]. Available: https://www.researchgate.net/publication/337305024_Artificial_Intelligence_in_Insurance_Sector
Abhijeet Urunkar, et al., "Fraud Detection and Analysis for Insurance Claim using Machine Learning," IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9774071
Umar Isa Abdulkadir, et al., "A Deep Learning Model for Insurance Claims Predictions," Journal on Artificial Intelligence 2024. [Online]. Available: https://www.techscience.com/jai/v6n1/56221/html
Devidas Kanchetti, et al., "Optimization Of Insurance Claims Management Processes Through The Integration Of Predictive Modeling And Robotic Process Automation," International Journal of Computer Applications (IJCA) Volume 2, Issue 2, July-December 2021. [Online]. Available: https://www.researchgate.net/publication/383987572_OPTIMIZATION_OF_INSURANCE_CLAIMS_MANAGEMENT_PROCESSES_THROUGH_THE_INTEGRATION_OF_PREDICTIVE_MODELING_AND_ROBOTIC_PROCESS_AUTOMATION
Aman Dubey, et al., "Smart Underwriting System: An Intelligent Decision Support System for Insurance Approval & Risk Assessment," IEEE 3rd International Conference for Convergence in Technology (I2CT), 2018. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8529792
Najmeddine Dhieb, et al., "A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement," IEEE Access ( Volume: 8), 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9046765
Rakibul Hasan Chowdhury, "Advancing fraud detection through deep learning: A comprehensive review ," World Journal of Advanced Engineering Technology and Sciences, 2024, 12(02), 606–613. [Online]. Available: https://wjaets.com/sites/default/files/WJAETS-2024-0332.pdf
Sravan Kumar Pala, et al., "Investigating Fraud Detection in Insurance Claims using Data Science," International Journal of Enhanced Research in Science, Technology & Engineering, Vol. 11 Issue 3, March-2022. [Online]. Available: https://www.erpublications.com/uploaded_files/download/sravan-kumar-pala_KkpnV.pdf
Sushant Kumar, et al., "Applications, Challenges, and Future Directions of Human-in-the-Loop Learning," IEEE Transactions on Industry Applications, vol. 60, no. 1, pp. 912-925, 2024. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10530996
Jiaming Zuo, "Mitigate Biased Decision-Making in AI Algorithms," Society of Actuaries Research Report, pp. 1-28, 2024. [Online]. Available: https://www.soa.org/4a3f62/globalassets/assets/files/resources/research-report/2024/ai-risk-essays/zuo-mitigate-biased-decision.pdf
Changli Zhang, et al., "Insurance-Based Cloud Computing-Architecture, Risk Analysis and Experiment," International Conference on Computational Intelligence and Software Engineering, 2010. [Online]. Available: https://ieeexplore.ieee.org/document/5676964
Ahmad Yahiya Ahmad Bani Ahmad, "Fraud Prevention in Insurance: Biometric Identity Verification and AI-Based Risk Assessment," IEEE International Conference on Knowledge Engineering and Communication Systems (ICKECS), 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10616613
Christopher P. Holland, "A Critical Success Factors (CSFs) Model for Next-Generation Artificial Intelligence (AI) Systems in Insurance Markets," DR CP HOLLAND 2022. [Online]. Available: https://www.techngi.uk/wp-content/uploads/2022/12/Critical-Success-Factors-3.1.pdf
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Copyright (c) 2025 Vasudev Daruvuri (Author)
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