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.

中文翻译：

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基于新颖的混合模型和神经网络的未决索赔计数的微观预测

预测未决索赔数量（IBNR）是精算损失准备金的核心问题。像链梯法这样的经典方法依赖于以损失三角形的形式聚合可用数据，从而浪费了潜在有用的附加索赔信息。提出了一种基于微观模型的新方法，用于报告涉及神经网络的延迟。通过广泛的模拟实验和对涉及汽车法律保险索赔的大规模真实数据集的应用表明，新方法在非同质投资组合的情况下提供了更准确的预测。