Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258094 
Year of Publication: 
2021
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 1 [Article No.:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-26
Publisher: 
MDPI, Basel
Abstract: 
In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.
Subjects: 
machine learning
neural networks
property and casualty insurance
ratemaking
reserving
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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