This article explores the potential of generative AI (GenAI) to support actuarial practice through four implemented case studies. It situates these case studies within the broader evolution of artificial intelligence in actuarial science, from early neural networks and machine learning to modern transformer-based GenAI systems. The first case study illustrates how large language models (LLMs) can improve claim cost prediction by extracting informative features from unstructured text for use in the underlying supervised learning task. The second case study demonstrates the automation of market comparisons using Retrieval-Augmented Generation to identify, extract, and structure relevant information from insurers' annual reports. The third case study highlights the capabilities of fine-tuned vision-enabled LLMs in classifying car damage types and extracting contextual information from images. The fourth case study presents a multi-agent system that autonomously migrates actuarial legacy code from R to Python and validates the translation against the original code's outputs. In addition to these case studies, we outline further GenAI applications in the insurance industry. Finally, we discuss the regulatory, security, dual-use and fraud, reproducibility, privacy, governance, and organisational challenges associated with deploying GenAI in regulated insurance environments.

翻译：本文通过四个已实施的案例研究，探索了生成式人工智能（GenAI）支持精算实践的潜力。文章将这些案例研究置于精算科学中人工智能更广泛的发展历程中，从早期神经网络和机器学习到现代基于Transformer的GenAI系统。第一个案例研究展示了大型语言模型（LLMs）如何通过从非结构化文本中提取信息特征以用于底层监督学习任务，从而改进索赔成本预测。第二个案例研究演示了如何利用检索增强生成技术实现市场比较自动化，从保险公司年度报告中识别、提取并结构化相关信息。第三个案例研究突出了经过微调、具备视觉能力的LLMs在分类车损类型及从图像中提取上下文信息方面的能力。第四个案例研究展示了一个多智能体系统，该系统能够自主将精算遗留代码从R语言迁移至Python，并根据原始代码的输出验证翻译结果。除这些案例研究外，我们还概述了生成式AI在保险行业中的进一步应用。最后，我们讨论了在受监管的保险环境中部署GenAI所面临的监管、安全、双重用途与欺诈、可重复性、隐私、治理及组织方面的挑战。