AI 中文总结
本研究利用LLM提取的网络小猫事件数据，验证网络经济损失符合幂律分布，并外推估计极端灾难事件的频率与损失规模，为网络保险风险建模提供定量依据。
AI 中文摘要
网络保险需要对最坏情况的灾难性（cat）事件进行准确建模，但该领域缺乏用于估计上限损失的稳健定量方法。基于近期一个包含30年内24起网络灾难事件的数据集，本研究检验网络经济损失是否遵循幂律分布。我们分析了“网络小猫”——即规模低于10亿美元的事件，与灾难事件（10亿美元以上）仅因规模大小而区分——这些事件通过LLM从网络保险索赔数据（2020-2024年）中提取。使用受害者数量（按索赔年份加权）作为经济损失的代理指标，我们将小猫级事件与已知灾难事件关联以估计损失。小猫分布与灾难数据集一致，且幂律在统计上具有合理性：事件规模每增加一个数量级，其发生概率下降5至7倍。外推结果表明，规模为2020-2024年最大灾难事件100倍的事件预计约每206年发生一次，对应1000至2500亿美元的损失——这虽属灾难性，但相对于其他保险险种而言并非异常。
英文摘要
Cyber insurance requires accurate modeling of worst-case catastrophic (cat) events, but the field lacks robust quantitative approaches for estimating upper-bound losses. Building on a recent dataset of 24 cyber cat events over 30 years, this work tests whether cyber economic losses follow a power law distribution. We analyze "cyber kittens" - sub-1B USD events distinguished from cat events (1B+ USD) only by magnitude - extracted via LLM from cyber insurance claims data (2020-2024). Using victim count (weighted by claim year) as a proxy for economic loss, we link kitten-sized events to known cat events to estimate losses. The kitten distribution proved consistent with the cat dataset, and power laws were statistically plausible: each order-of-magnitude increase in event size corresponds to a 5-7x drop in probability. Extrapolating, an event 100x the largest 2020-2024 cat event is expected roughly every 206 years, translating to 100-250B USD in losses - catastrophic, but not extraordinary relative to other insurance lines.
Comments11 pages, 3 figures, 2 tables
