# Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance

arXiv: https://arxiv.org/abs/2201.02397 · HTML: https://mlsanity.com/paper/2201.02397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 18 of 18)

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## Abstract (arXiv)

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.