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.
Highlights
The use of statistical learning models has been a common practice in actuarial science since the 1980s
We review nearly a hundred articles and case studies using machine learning in property and casualty insurance
This paper reviewed the literature on pricing and reserving for Property and casualty insurance (P&C) insurance
Summary
The use of statistical learning models has been a common practice in actuarial science since the 1980s. A case study comparing machine learning models for ratemaking was conducted by Dugas et al (2003), who compared five classes of models: linear regression, generalized linear models, decision trees, neural networks and support vector machines. From their concluding remarks, we read, “We hope this paper goes a long way towards convincing actuaries to include neural networks within their set of modeling tools for ratemaking.”. We read, “We hope this paper goes a long way towards convincing actuaries to include neural networks within their set of modeling tools for ratemaking.” It took 15 years for this suggestion to be noticed. Quoted reasons for this resurgence include introducing better activation functions, datasets composed of many more images, and much more powerful GPUs LeCun et al (2015)
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