isaaccs / prediction-of-the-outcome-of-insurance-claims-with-deep-neural-networks Goto Github PK
View Code? Open in Web Editor NEWIn this paper, we develop a methodology to perform the predic- tion on an open insurance claim (RBNS, Reported But Not Settled) from a set of complex covariates with various structures (structured and unstructured data). The technique combines different deep neu- ral networks architectures (such as Long Short Term Memory for text data) with survival analysis prediction methods (to predict the time of settlement of the claim). The deep learning methods are used to extract features from our complex data, hence to perform dimension reduction. These features may be plugged in a final neural network predictor, or combined with more intelligible models like a General- ized Linear Model, if the need for interpretation is more important than the quality of the prediction. A real data analysis illustrates the technique.
License: MIT License