发表机构
University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述基础模型在保险风险建模中的应用，提出将语言、视觉、科学等模型输出连接至精算计算并评估其贡献与合规性的流程，以利用多源数据改善损失估计。
AI 中文摘要
索赔叙述、图像和传感器数据包含有关承保风险的信息，这些信息难以通过现有的精算模型加以利用。基础模型从大型数据集中学习模式，然后再适应特定任务。通过将这些高维数据源转化为变量或数值表示，它们可以帮助保险公司利用更多已收集的信息，从而可能减少开发每个应用所需的经验。例如，语言模型可以在新的索赔说明中识别出伤情的恶化，使准备金模型能够在赔付揭示恶化之前识别预期成本的变动。在本文中，我们回顾了语言、视觉、地理空间、时间序列、表格和科学模型，解释了现有的保险应用和潜在的未来用途。科学模型将此方法扩展到未来的天气和气候条件：一旦将局部灾害与资产损失、维修成本和保险覆盖范围联系起来，其模拟结果可以为损失估计提供信息。我们提出了一个将这些模型输出连接到精算计算并评估其预测贡献、稳定性和信息使用规则合规性的流程。当最终索赔成本在长时间延迟后才为人所知、大额损失罕见或从其他地方学到的模式无法转移到目标组合时，评估这些应用是困难的。更丰富的数据可以揭示私人信息并支持更精细的风险分类，这可能会改变获得保险的机会。在保险公司之间重用相同的模型也造成了对共享预测和提供商的依赖。
英文摘要
Claim narratives, images and sensor data contain information about insured risks that is difficult to use through existing actuarial models. Foundation models learn patterns from large datasets before being adapted to particular tasks. By turning these high-dimensional sources into variables or numerical representations, they could help insurers use more of the information they already collect, potentially reducing the experience needed to develop each application. For example, a language model could identify a worsening injury in a new claim note, allowing a reserving model to recognise the change in expected cost before the payments reveal the deterioration. In this paper, we review language, vision, geospatial, time series, tabular and scientific models, explaining existing insurance applications and potential future uses. Scientific models extend this approach to future weather and climate conditions: their simulations can inform loss estimates once local hazards are linked to asset damage, repair costs and insurance coverage. We propose a process to connect these model outputs to actuarial calculations and to assess their predictive contribution, stability and compliance with rules on information use. Evaluating these applications is difficult when final claim costs become known only after long delays, large losses are rare or patterns learned elsewhere fail to transfer to the target portfolio. Richer data can reveal private information and support finer risk classification, which can change access to insurance. Reusing the same models across insurers also creates dependence on shared predictions and providers.
