隐藏不均匀马尔科夫链的神经校准：人寿保险中的信息解压缩

IF 4.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Machine Learning Pub Date : 2024-07-31 DOI:10.1007/s10994-024-06551-w

Mark Kiermayer, Christian Weiß

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引用次数: 0

摘要

马尔可夫链在包括人寿保险数学在内的众多领域发挥着关键作用。保费值等标准精算量可以解释为有关底层马尔可夫过程的压缩、有损信息。我们介绍了一种方法，可以根据合同组合的集体信息重建底层马尔可夫链。我们的神经架构通过明确提供一步转换概率，以高度可解释的方式描述了该过程。此外，我们还提供了一个内在的经济模型验证，以检查信息解压缩的质量。最后，我们的方法在德国定期人寿保险合同的现实数据集上进行了成功测试。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Neural calibration of hidden inhomogeneous Markov chains: information decompression in life insurance

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Neural calibration of hidden inhomogeneous Markov chains: information decompression in life insurance

Markov chains play a key role in a vast number of areas, including life insurance mathematics. Standard actuarial quantities as the premium value can be interpreted as compressed, lossy information about the underlying Markov process. We introduce a method to reconstruct the underlying Markov chain given collective information of a portfolio of contracts. Our neural architecture characterizes the process in a highly explainable way by explicitly providing one-step transition probabilities. Further, we provide an intrinsic, economic model validation to inspect the quality of the information decompression. Lastly, our methodology is successfully tested for a realistic data set of German term life insurance contracts.

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来源期刊

Machine Learning 工程技术-计算机：人工智能

CiteScore

11.00

自引率

2.70%

发文量

162

审稿时长

3 months

期刊介绍： Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.