Introduction & Mortality Estimation, Markov Chains and Deep Learning

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.