Mortality Estimation, Markov Chains and Deep Learning Standard actuarial quantities as the premium value can be interpreted as compressed, lossy information about the underlying Markov process. We introduce a machine learning 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. Our methodology is successfully tested for a realistic data set of German term life insurance contracts. Further, we provide an intrinsic, economic model validation to inspect the quality of the information decompression.