用LSTM检测保险赔付中的结构突变，提升气候灾害频发年份的预测精度。
LSTM-Based Detection of Structural Breaks in Property Insurance Loss Reserving: A Climate-Informed Approach
- 用LSTM模型识别气候灾害导致的赔付数据突变，替代传统精算方法。
- 在佛罗里达和路易斯安那州数据上，灾年预测误差降低15%~20%。
- 首次为LSTM在保单准备金中的应用提供概率理论保障，适合风险建模者参考。
准确的赔付准备金估算关乎保险公司偿付能力，但日益加剧的气候灾难正系统性破坏传统精算方法所依赖的稳定性假设。本白皮书提出一项研究计划，测试长短期记忆（LSTM）神经网络是否能比链梯法、Bornhuetter Ferguson法及Cape Cod法更快速、更准确地检测并适应这些结构突变。基于佛罗里达州与路易斯安那州超过15年的监管发展三角数据，结合美国国家海洋和大气管理局（NOAA）的飓风强度指数与海表温度数据，我们假设在高灾害暴露年份，准备金预测精度可提升15%至20%，这一阈值既源于先前神经网络精算研究，也基于本文建立的形式化收敛结果。除实证验证外，我们还构建了将LSTM结构突变检测置于概率框架下的理论体系，为测试期内有限的灾难事件提供了形式化性能保证。文中详述研究设计、方法、预期贡献，并坦诚评估局限性。
原文摘要 · Abstract (English)
Accurate loss reserving is foundational to insurer solvency, yet accelerating climate driven catastrophes systematically violate the stability assumptions on which traditional actuarial methods depend. This white paper presents a research program testing whether Long Short Term Memory (LSTM) neural networks can detect and adapt to these structural breaks faster and more accurately than Chain Ladder, Bornhuetter Ferguson, and Cape Cod methods. Using 15 plus years of regulatory development triangle data from Florida and Louisiana, enriched with NOAA hurricane intensity indices and sea surface temperatures, we hypothesize a targeted improvement of 15, 20% in reserve accuracy for catastrophe exposed years, a threshold grounded both in the prior neural network reserving literature and in the formal convergence results developed here. Beyond empirical validation, we develop a theoretical framework grounding LSTM structural break detection in probabilistic terms, providing formal performance guarantees that compensate for the limited number of catastrophe events in the test period. We document the research design, methodology, expected contributions, and a candid assessment of limitations.
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