DeepTriangle: A Deep Learning Approach to Loss Reserving

TL;DR

DeepTriangle introduces a deep learning method for loss reserving that jointly models paid losses and claims, improving predictive accuracy with minimal feature engineering and enabling automation.

Contribution

It presents a novel deep neural network approach for loss reserving that outperforms traditional stochastic methods and simplifies the modeling process.

Findings

Improved predictive accuracy over existing methods

Models require minimal feature engineering

Enables more frequent automated forecasts

Abstract

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.