LLMs and Agentic AI in Insurance Decision-Making: Opportunities and Challenges For Africa

• URL: http://arxiv.org/abs/2508.15110v1
• Date: Wed, 20 Aug 2025 22:57:00 GMT
• Title: LLMs and Agentic AI in Insurance Decision-Making: Opportunities and Challenges For Africa
• Authors: Graham Hill, JingYuan Gong, Thulani Babeli, Moseli Mots'oehli, James Gachomo Wanjiku,
• Abstract summary: We consider and emphasize the unique opportunities, challenges, and potential pathways in insurance.<n>We identify critical gaps in the African insurance market and highlight key local efforts, players, and partnership opportunities.<n>We call upon actuaries, insurers, regulators, and tech leaders to a collaborative effort aimed at creating inclusive, sustainable, and equitable AI strategies and solutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we highlight the transformative potential of Artificial Intelligence (AI), particularly Large Language Models (LLMs) and agentic AI, in the insurance sector. We consider and emphasize the unique opportunities, challenges, and potential pathways in insurance amid rapid performance improvements, increased open-source access, decreasing deployment costs, and the complexity of LLM or agentic AI frameworks. To bring it closer to home, we identify critical gaps in the African insurance market and highlight key local efforts, players, and partnership opportunities. Finally, we call upon actuaries, insurers, regulators, and tech leaders to a collaborative effort aimed at creating inclusive, sustainable, and equitable AI strategies and solutions: by and for Africans.

Related papers

• Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
  This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
  arXiv  Detail & Related papers  (2026-02-16T04:30:06Z)
• Building Capacity for Artificial Intelligence in Africa: A Cross-Country Survey of Challenges and Governance Pathways [0.0]
  Artificial intelligence (AI) is transforming education and the workforce, but access to AI learning opportunities in Africa remains uneven.<n>This study investigates how universities and industries engage in shaping AI education and workforce preparation.<n>Survey responses from five African countries (Ghana, Namibia, Rwanda, Kenya and Zambia)
  arXiv  Detail & Related papers  (2025-12-05T05:14:23Z)
• Embodied AI: Emerging Risks and Opportunities for Policy Action [46.48780452120922]
  Embodied AI (EAI) systems can exist in, learn from, reason about, and act in the physical world.<n>EAI systems pose significant risks, including physical harm from malicious use, mass surveillance, as well as economic and societal disruption.
  arXiv  Detail & Related papers  (2025-08-28T17:59:07Z)
• AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing [8.195356684218691]
  AI agents are autonomous systems designed to perceive, reason, and act within dynamic environments.<n>LLMs, MLLMs, and Agentic AI contribute to expanding AI's capabilities in information processing, environmental perception, and autonomous decision-making.<n>This study systematically reviews the evolution of AI and AI agent technologies.
  arXiv  Detail & Related papers  (2025-07-02T05:31:17Z)
• A Framework for the Assurance of AI-Enabled Systems [0.0]
  This paper proposes a claims-based framework for risk management and assurance of AI systems.<n>The paper's contributions are a framework process for AI assurance, a set of relevant definitions, and a discussion of important considerations in AI assurance.
  arXiv  Detail & Related papers  (2025-04-03T13:44:01Z)
• Superintelligence Strategy: Expert Version [64.7113737051525]
  Destabilizing AI developments could raise the odds of great-power conflict.<n>Superintelligence -- AI vastly better than humans at nearly all cognitive tasks -- is now anticipated by AI researchers.<n>We introduce the concept of Mutual Assured AI Malfunction.
  arXiv  Detail & Related papers  (2025-03-07T17:53:24Z)
• AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources [24.502423087280008]
  We introduce the AI Risk Atlas, a structured taxonomy that consolidates AI risks from diverse sources and aligns them with governance frameworks.<n>We also present the Risk Atlas Nexus, a collection of open-source tools designed to bridge the divide between risk definitions, benchmarks, datasets, and mitigation strategies.
  arXiv  Detail & Related papers  (2025-02-26T12:23:14Z)
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• Safety challenges of AI in medicine in the era of large language models [23.817939398729955]
  Large language models (LLMs) offer new opportunities for medical practitioners, patients, and researchers.<n>As AI and LLMs become more powerful and especially achieve superhuman performance in some medical tasks, public concerns over their safety have intensified.<n>This review examines emerging risks in AI utilization during the LLM era.
  arXiv  Detail & Related papers  (2024-09-11T13:47:47Z)
• Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
  GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)<n>We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.<n>We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
  arXiv  Detail & Related papers  (2024-04-25T15:59:16Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• International Institutions for Advanced AI [47.449762587672986]
  International institutions may have an important role to play in ensuring advanced AI systems benefit humanity. This paper identifies a set of governance functions that could be performed at an international level to address these challenges. It groups these functions into four institutional models that exhibit internal synergies and have precedents in existing organizations.
  arXiv  Detail & Related papers  (2023-07-10T16:55:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.