Deploying Agentic AI Across Insurance and Retirement Products
DOI:
https://doi.org/10.5281/zenodo.20591549Keywords:
Artificial intelligence · Strategic decision-making · Insurance and retirement · Enterprise data product · Data infrastructure · Agentic AI.Abstract
The integration of agentic AI into enterprise data products, following established principles of strategic decision-making, significantly improves agent-in-the-loop decision-making within insurance and retirement sectors. Decision-making systems underpin a broad spectrum of enterprise intelligence solutions, enabling human decision-makers to harness advanced AI technology, data analytics, and internal expertise. However, agent-in-the-loop solutions must address the same set of decision-making challenges that AI systems tackle independently—commanding consideration during the architecture design phase, especially in data products integrated for concurrent consumption within enterprises.
Within enterprise data products, agentic AI in support of strategic decision-making exposes tertiary product dimensions that determine the product's trustworthiness, its behavioral alignment with user expectations, the underlying technical infrastructure, and the managed change required for adoption and operation. It serves as a lens for examining enterprise data products structured for concurrent syndication, spotlighting product characteristics that ensure decision-making processes yield agent-in-the-loop systems of record supported by AI-assistive technologies and the agents' governing processes. The approach follows convergence techniques mirrored in industries such as cybersecurity that also underpin enterprise data products serving strategic decision-making in the insurance and retirement spaces.
References
[1] Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv.
[2] Chen, L., Zaharia, M., & Zou, J. (2021). How is ChatGPT’s behavior shaped by reinforcement learning from human feedback? ACM Queue, 21(1).
[3] Davenport, T. H., & Mittal, N. (2022). All-in on AI: How smart companies win big with artificial intelligence. Harvard Business Review Press.
[4] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv.
[5] European Insurance and Occupational Pensions Authority. (2021). Artificial intelligence governance principles: Towards ethical and trustworthy AI in the insurance sector. EIOPA.
[6] Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694.
[7] Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining explanations: An overview of interpretability of machine learning. Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA).
[8] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[9] Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1–42.
[10] Hassan, N., & Jabeen, F. (2022). Explainable AI for insurance risk assessment: A review of interpretable models and applications. Expert Systems with Applications, 207, 117900.
[11] High-Level Expert Group on Artificial Intelligence. (2019). Ethics guidelines for trustworthy AI. European Commission.
[12] International Organization for Standardization. (2023). ISO/IEC 42001:2023 Artificial intelligence—Management system. ISO.
[13] International Organization for Standardization. (2022). ISO/IEC 23894:2022 Artificial intelligence—Risk management. ISO.
[14] International Organization for Standardization. (2022). ISO/IEC 22989:2022 Artificial intelligence—Concepts and terminology. ISO.
[15] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
[16] Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
[17] Kearns, M., & Roth, A. (2019). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press.
[18] Kroll, J. A. (2021). Out of the black box: The legal and ethical implications of algorithmic decision-making. Harvard Journal of Law & Technology, 34(1), 1–43.
[19] Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
[20] Mikalef, P., Fjørtoft, S. O., & Torvatn, H. Y. (2021). Artificial intelligence in the decision-making context: A systematic review. Information Systems Frontiers, 23, 1–25.
[21] Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT).
[22] Molnar, C. (2022). Interpretable machine learning (2nd ed.). Independently published.
[23] Nasseri, M., & Alizadeh, M. (2023). AI-driven underwriting and claims automation: Opportunities and risks in insurance operations. The Geneva Papers on Risk and Insurance—Issues and Practice, 48(3), 501–527.
[24] National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST.
[25] National Institute of Standards and Technology. (2023). AI RMF Playbook. NIST.
[26] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
[27] Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAccT).
[28] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
[29] Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
[30] Shapiro, S. S., & Jain, H. (2022). Algorithmic governance and compliance in financial and insurance services: A practical framework. Journal of Financial Regulation and Compliance, 30(3), 312–331.
[31] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
[32] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. International Conference on Learning Representations (ICLR).
[33] Tariq, H., & Khan, M. (2021). Predictive analytics and machine learning for retirement planning: A systematic literature review. Decision Support Systems, 142, 113474.
[34] The Geneva Association. (2020). Artificial intelligence and insurance: Global perspectives on governance and regulation. The Geneva Association.
[35] Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
[36] Varian, H. R. (2018). Artificial intelligence, economics, and industrial organization. In A. K. Agrawal, J. Gans, & A. Goldfarb (Eds.), The economics of artificial intelligence: An agenda (pp. 399–419). University of Chicago Press.
[37] Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887.
[38] World Economic Forum. (2021). Global AI action alliance: Guidelines for responsible AI adoption. World Economic Forum.
[39] Zheng, L., Li, J., & Wang, X. (2024). Agentic LLM systems for enterprise decision support: Architecture patterns and risk controls. IEEE Software, 41(2), 45–54.
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