Neural Calibration of Hidden Inhomogeneous Markov Chains: Information Decompression in Life Insurance
Abstract
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.
Cite
Text
Kiermayer and Weiß. "Neural Calibration of Hidden Inhomogeneous Markov Chains: Information Decompression in Life Insurance." Machine Learning, 2024. doi:10.1007/S10994-024-06551-WMarkdown
[Kiermayer and Weiß. "Neural Calibration of Hidden Inhomogeneous Markov Chains: Information Decompression in Life Insurance." Machine Learning, 2024.](https://mlanthology.org/mlj/2024/kiermayer2024mlj-neural/) doi:10.1007/S10994-024-06551-WBibTeX
@article{kiermayer2024mlj-neural,
title = {{Neural Calibration of Hidden Inhomogeneous Markov Chains: Information Decompression in Life Insurance}},
author = {Kiermayer, Mark and Weiß, Christian},
journal = {Machine Learning},
year = {2024},
pages = {7129-7156},
doi = {10.1007/S10994-024-06551-W},
volume = {113},
url = {https://mlanthology.org/mlj/2024/kiermayer2024mlj-neural/}
}