  We propose a novel approach for loss reserving based on deep neural networks.
The approach allows for joint modeling of paid losses and claims outstanding,
and incorporation of heterogeneous inputs. We validate the models on loss
reserving data across lines of business, and show that they improve on the
predictive accuracy of existing stochastic methods. The models require minimal
feature engineering and expert input, and can be automated to produce forecasts
more frequently than manual workflows.
