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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Leveraging AI for sustainable risk assessment in the insurance industry

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pp. 2499–2510Vol. 46Issue 8November 2025DOI: 10.47974/JIOS-2067XML
Received:
08 Jan 2025
Published Online:
29 Nov 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2067
Pages:
2499–2510

Abstract

The context of introducing Artificial Intelligence technologies to assess risks has thrown the insurance industry into a transitional mode across industries. Fast replacing the AI-based models, traditional methodologies provide not only better accuracy but also efficiency and sustainability in assessing risks. The paper further explains how various AI tools help sustain risk assessment within the insurance industry. Large data sets and real-time information, facilitated by AI, enable the easy identification of emerging risks, optimization of underwriting processes [1], and the development of custom insurance product plans. Moreover, AI-based proactive risk management enhances financial performance with ESG goals. Thus, the paper presents a case study in the form of real-life applications that highlight how AI can mitigate the negative impacts on the environment and social realms, ensuring resiliency and sustainability [2] for the long-term business plan. It addresses issues of data privacy, algorithmic bias, and regulatory compliance [3], emphasizing the importance of responsible AI development [4]. The findings draw the right use of AI towards sustainable development paradigms, thereby creating more resilience and responsibility in the insurance industry.

Keywords

Subject Classifications

68T0591G0568T50

References

[1] J. Fleischer and E. Lawson, “Ethical frameworks for AI-driven underwriting in microinsurance,” Microinsurance Innov. Rev., vol. 3, no. 2, pp. 19–36 (2021).
[2] S. Allred and W. Ling, “Sustainability-oriented product design in the insurance sector: The role of AI-driven metrics,” Sustain. Bus. J., vol. 5, no. 2, pp. 33–47 (2019).
[3] T. Johnson and R. Scully, “AI compliance strategies: Insurance regulations in the digital age,” Compliance \& Risk Rev., vol. 6, no. 3, pp. 92–105 (2019).
[4] B. Friedman and G. Lane, “Developing interpretable AI models for life insurance underwriting,” J. Risk Model., vol. 15, no. 1, pp. 11–26 (2020).
[5] D. E. Bland, “Risk management in insurance,” J. Financ. Regul. Compliance, vol. 7, no. 1, pp. 13–16 (1999), doi: 10.1108/EB024991.
[6] V. Lyubchich, N. K. Newlands, A. Ghahari, T. Mahdi, and Y. R. Gel, “Insurance risk assessment in the face of climate change: Integrating data science and statistics,” Wiley Interdiscip. Rev. Comput. Stat., vol. 11, no. 4 (2019), doi: 10.1002/WICS.1462.
[7] Y. Huang and Q. Chang, “Analysis and Application of Decision Models Based on Risk Assessment and Insurance Pricing,” Trans. Econ. Bus. Manag. Res., vol. 11, pp. 311–319 (2024), doi: 10.62051/j7q1g497.
[8] M. Maier, H. Carlotto, F. Sanchez, S. Balogun, and S. Merritt, “Transforming Underwriting in the Life Insurance Industry,” Proc. AAAI Conf. Artif. Intell., vol. 33, no. 01, pp. 9373–9380 (2019), doi: 10.1609/AAAI.V33I01.33019373.
[9] N. Fenton and M. Neil, “The use of Bayes and causal modelling in decision making, uncertainty and risk,” (2011).
[10] T. Roberts, “The use of credit scorecard design, predictive modelling and text mining to detect fraud in the insurance industry.” (2011).
[11] S. R. Adavelli, “Beyond the Claims: Emerging AI Models and Predictive Analytics in Property & Casualty Insurance Risk Assessment,” Int. J. Sci. Res., vol. 13, no. 7, pp. 1625–1631 (2024), doi: 10.21275/sr24077085515.
[12] H. Lee, “Risk management practices and challenges in insurance,” J. Risk Assess., vol. 22, no. 1, pp. 34–47 (2017).
[13] C. Kelley and K. Wang, “InsurTech: A Guide for the Actuarial Community.” (2021).
[14] X. Liu, Y. Zhang, and T. Wang, “The cost of manual risk assessments: A quantitative study in the insurance industry,” J. Insur. Econ., vol. 5, no. 2, pp. 67–80 (2018).
[15] G. Brandt and T. Reichel, “Automating actuarial tasks: An AI-based approach,” Ann. Actuar. Sci., vol. 12, no. 1, pp. 45–62 (2018).
[16] R. Jones and J. Smith, “The evolving landscape of risk assessment in insurance: A critical review,” Risk Manag. Rev., vol. 15, no. 4, pp. 79–92 (2020).
[17] M. Doshi and A. Patel, “Leveraging AI and IoT for proactive home insurance risk reduction,” Insur. IoT & Anal. J., vol. 7, no. 1, pp. 55–70 (2022).
[18] L. Fraser and A. Gupta, “Assessing ESG factors with AI in commercial property insurance,” Sustain. Insur. Q., vol. 7, no. 2, pp. 33–50 (2022).

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