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DeepTriangle: A Deep Learning Approach to Loss Reserving

Kasa AI, 3040 78th Ave SE #1271, Mercer Island, WA 98040, USA
Risks 2019, 7(3), 97; https://doi.org/10.3390/risks7030097
Received: 15 August 2019 / Revised: 7 September 2019 / Accepted: 12 September 2019 / Published: 16 September 2019
(This article belongs to the Special Issue Claim Models: Granular Forms and Machine Learning Forms)
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. View Full-Text
Keywords: loss reserving; machine learning; neural networks loss reserving; machine learning; neural networks
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Kuo, K. DeepTriangle: A Deep Learning Approach to Loss Reserving. Risks 2019, 7, 97.

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