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.

中文翻译：

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精算科学中的人工智能——最新进展回顾——第 1 部分

人工智能 (AI) 和机器学习的快速发展正在创造产品和服务，这些产品和服务不仅有可能改变精算师的工作环境，而且还可能在精算科学中提供新的机会。这些进步基于设计、拟合和应用神经网络的现代方法，通常称为“深度学习”。本文研究了精算科学如何在未来几年内适应和发展以整合这些新技术和方法。本文的第 1 部分提供了机器学习和深度学习的背景，以及精算师可能从应用这些技术中受益的启发式方法。论文的第 2 部分随后调查了人工智能在精算科学中的新兴应用，并举例说明了死亡率建模、索赔准备金、非寿险定价和远程信息处理。对于一些示例，已在 GitHub 上提供了代码，以便感兴趣的读者可以自己试验这些技术。第 2 部分最后展望了精算师将深度学习融入其活动的潜力。最后，补充附录讨论了进一步的资源，提供了机器学习和深度学习的更深入背景。