Title: DeepTriangle: A Deep Learning Approach to Loss Reserving,
Abstract: We propose a novel approach for loss reserving based on deep neural networks.
The approach allows for jointly modeling of paid losses and claims outstanding,
and incorporation of heterogenous inputs. We validate the models on loss
reserving data across lines of business, and show that they attain or exceed
the predictive accuracy of existing stochastic methods. The models require
minimal feature engineering and expert input, and can be automated to produce
forecasts at a high frequency.