Bücher Axel, Rosenstock Alexander
Mathematisches Institut, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.
ARAG SE, ARAG-Platz 1, 40472 Düsseldorf, Germany.
Eur Actuar J. 2023;13(1):55-90. doi: 10.1007/s13385-022-00314-4. Epub 2022 May 12.
Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor legal insurance claims that the new approach provides more accurate predictions in case of non-homogeneous portfolios.
The online version contains supplementary material available at 10.1007/s13385-022-00314-4.
预测未决赔款（IBNR）是精算损失准备金领域的核心问题。像链梯法这样的传统方法依赖于以损失三角形的形式汇总可用数据，从而浪费了潜在有用的额外索赔信息。本文提出了一种基于涉及神经网络的报告延迟微观模型的新方法。通过大量模拟实验以及对一个涉及汽车法定保险索赔的大规模真实数据集的应用表明，在非同质投资组合的情况下，新方法能提供更准确的预测。
在线版本包含可在10.1007/s13385-022-00314-4获取的补充材料。
