  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.
