Transitioning from Static Rules to AI-Driven Context-Aware Decision Support in Life Insurance Platforms
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P115Keywords:
Context-Aware Intelligence, Autonomous Underwriting, Life Insurance Technology, Explainable AI (Xai), Interpretable Machine Learning, Risk-Integrated Engines, Actuarial Modernization, Algorithmic Fairness, Real-Time Data StreamsAbstract
Traditional life insurance decisioning frameworks are predominantly governed by static, rule-based heuristics that fail to capture the high-dimensional, non-linear correlations inherent in modern policyholder data. The proposed CORE (Context-Aware Optimization and Rule-Integrated Engine) is a new hybrid DSS that will facilitate the transformation of industry practices from traditional static actuarial rules-based systems to dynamic, intelligent systems that utilize AI and are responsive to the context of an individual's behavior. Through the integration of machine learning algorithms such as ensemble techniques, including Gradient Boosting and Random Forests, with a proprietary Context Engine, CORE captures relevant contextual information in real-time by analyzing behavioral, environmental, and time-related factors, providing detailed risk assessments on each individual. The XAI methodological approach was developed to be both transparent and compliant with regulatory requirements in high-risk industries where the lack of interpretability can have serious consequences. The CORE DSS includes a multi-layered design, consisting of a data-driven predictive layer for estimating risks and a rule-integrated validation layer for validating compliance with current state regulations and mandated insurance policies. Experimental results demonstrate that this hybrid approach significantly enhances predictive accuracy and decision efficiency while maintaining the ethical and fairness standards required for life insurance platforms. This research provides a scalable technical blueprint for the evolution of autonomous, context-driven financial journeys.
References
[1] I. H. Sarker, A. I. Khan, Y. B. Abushark, and F. Alsolami, “Mobile expert system: Exploring context-aware machine learning rules for personalized decision-making in mobile applications,” Symmetry, vol. 13, no. 10, p. 1975, 2021. [Online]. Available: https://www.mdpi.com/2073-8994/13/10/1975
[2] M. Mishra, D. Sidoti, G. V. Avvari, P. Mannaru, D. F. M. Ayala, and K. R. Pattipati, “A context-driven framework for proactive decision support with applications,” IEEE Access, vol. 5, pp. 12475–12495, May 2017. [Online]. Available: https://ieeexplore.ieee.org/document/7932848
[3] R. A. Cajo Diaz, M. Ghita, D. Copot, I. R. Birs, C. Muresan, and C. Ionescu, “Context aware control systems: An engineering applications perspective,” IEEE Access, vol. 8, pp. 215550–215569, Nov. 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9272959
[4] Ren, J. (2021). Research on financial investment decision based on artificial intelligence algorithm. IEEE Sensors Journal, 21(22), 25190–25197. https://doi.org/10.1109/jsen.2021.3104038
[5] O. H. Olayinka, “Data-driven customer segmentation and personalization strategies in modern business intelligence frameworks,” World J. Adv. Res. Rev., vol. 12, no. 3, pp. 711–726, 2021. [Online]. Available: https://doi.org/10.30574/wjarr.2021.12.3.0658
[6] M. Riikkinen, H. Saarijärvi, P. Sarlin, and I. Lähteenmäki, “Using artificial intelligence to create value in insurance,” Int. J. Bank Mark., vol. 36, no. 6, pp. 1145–1168, 2018. [Online]. Available: https://www.emerald.com/ijbm/article-abstract/36/6/1145/188952
[7] C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” arXiv preprint arXiv:1811.10154, 2019. [Online]. Available: https://arxiv.org/abs/1811.10154
[8] R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, D. Pedreschi, and F. Giannotti, “A survey of methods for explaining black box models,” arXiv preprint arXiv:1802.01933, 2018. [Online]. Available: https://arxiv.org/abs/1802.01933
[9] A. Chancel, L. Bradier, A. Ly, R. Ionescu, L. Martin, and M. Sauce, “Applying machine learning to life insurance: Some knowledge sharing to master it,” arXiv preprint arXiv:2209.02057, 2022. [Online]. Available: https://arxiv.org/abs/2209.02057
[10] L. Barry and A. Charpentier, “The fairness of machine learning in insurance: New rags for an old man?,” arXiv preprint arXiv:2205.08112, 2022. [Online]. Available: https://arxiv.org/abs/2205.08112
