LocalGLMnet: A Deep Learning Architecture for Actuaries

Abstract

The purpose of this tutorial is to discuss the LocalGLMnet architecture which is tailored to the needs of actuaries. The LocalGLMnet is a flexible network architecture for tabular data that allows for variable selection, the study of interactions, gives nice interpretations and allows to rank variable importance. We explore a LocalGLMnet on accident insurance claims data for which we also have short claim descriptions. In a second step we try to understand the predictive power of these claim descriptions by adding a recurrent neural network layer to process the claim texts into tabular data.