AI in Actuarial Science

53 Pages Posted: 15 Aug 2018 Last revised: 20 Aug 2018

Date Written: July 24, 2018


Rapid advances in Artificial Intelligence 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. After providing some background on machine learning and deep learning, and providing a heuristic for where actuaries might benefit from applying these techniques, the paper 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. The paper concludes with an outlook on the potential for actuaries to integrate deep learning into their activities.

Keywords: Actuarial Science, Deep Learning, Machine Learning, Insurance, Telematics

JEL Classification: G22

Suggested Citation

Richman, Ronald, AI in Actuarial Science (July 24, 2018). Available at SSRN: or

Ronald Richman (Contact Author)

QED Actuaries and Consultants ( email )

38 Wierda Road West
Johannesburg, Gauteng 2196
South Africa

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