Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257935 
Authors: 
Year of Publication: 
2019
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 7 [Issue:] 3 [Article No.:] 97 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-12
Publisher: 
MDPI, Basel
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.
Subjects: 
loss reserving
machine learning
neural networks
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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