Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance
This work addresses the challenge of information decompression for actuaries in life insurance, offering a novel approach to improve premium calculations, though it is incremental in applying neural methods to a specific domain.
The authors tackled the problem of reconstructing underlying Markov chains from aggregated portfolio data in life insurance, introducing a neural method that explicitly provides transition probabilities and validating it on a realistic German term life insurance dataset.
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.