论文标题
隐藏不均匀马尔可夫连锁店的神经校准 - 人寿保险的信息减压
Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance
论文作者
论文摘要
马尔可夫连锁店在包括人寿保险数学在内的大量领域中起着关键作用。标准的精算量为高级价值可以解释为有关基础马尔可夫进程的压缩，有损的信息。我们介绍了一种方法，以给定合同投资组合的集体信息重建基础马尔可夫链。我们的神经体系结构可以通过明确提供一步过渡概率来解释该过程。此外，我们提供了一种内在的经济模型验证，以检查信息减压的质量。最后，我们的方法已成功测试了德国定期人寿保险合同的现实数据集。
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 explainably characterizes the process 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.
