Despite the high importance of grouping in practice, there exists little research on the respective topic. The present work presents a complete framework for grouping and a novel method to optimize model points. Model points are used to substitute clusters of contracts in an insurance portfolio and thus yield a smaller, computationally less burdensome portfolio. This grouped portfolio is controlled to have similar characteristics as the original portfolio. We provide numerical results for term life insurance and defined contribution plans, which indicate the superiority of our approach compared to K-means clustering, a common baseline algorithm for grouping. Lastly, we show that the presented concept can optimize a fixed number of model points for the entire portfolio simultaneously. This eliminates the need for any pre-clustering of the portfolio, e.g. by K-means clustering, and therefore presents our method as an entirely new and independent methodology.

中文翻译：

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使用神经网络对保险合同进行分组

尽管分组在实践中非常重要，但对相应主题的研究很少。目前的工作提出了一个完整的分组框架和一种优化模型点的新方法。模型点用于替代保险投资组合中的合同集群，从而产生更小、计算负担更轻的投资组合。这个分组的投资组合被控制为具有与原始投资组合相似的特征。我们提供了定期人寿保险和固定缴款计划的数值结果，这表明我们的方法与 K 均值聚类（一种常见的分组基线算法）相比具有优越性。最后，我们展示了所提出的概念可以同时优化整个投资组合的固定数量的模型点。这消除了对投资组合的任何预聚类的需要，