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AI in actuarial science – a review of recent advances – part 2

Published online by Cambridge University Press:  26 August 2020

Ronald Richman Open the ORCID record for Ronald Richman [Opens in a new window]
Ronald Richman*
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QED Actuaries and Consultants
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Email: Ronald.richman@qedact.com
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Abstract

Rapid advances in artificial intelligence (AI) and machine learning are creating products and services with the potential not only to change the environment in which actuaries operate, but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning”. This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. Part 1 of this paper provides background on machine learning and deep learning, as well as an heuristic for where actuaries might benefit from applying these techniques. Part 2 of the paper then surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. Part 2 concludes with an outlook on the potential for actuaries to integrate deep learning into their activities. Finally, a supplementary appendix discusses further resources providing more in-depth background on machine learning and deep learning.

Keywords

Actuarial scienceDeep learningMachine learningInsuranceTelematics
Type
Review
Information
Annals of Actuarial Science , Volume 15 , Issue 2 , July 2021 , pp. 230 - 258
Copyright
© Institute and Faculty of Actuaries 2020

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