By the end of July, a group of colleagues from Dalhousie and Saint Mary’s Universities in Halifax will host a series of online lectures on machine learning for economists and applied social scientists. I will be giving a talk on machine learning and insurance.
With Olivier Cabrignac and Ewen Gallic, we recently uploaded a research paper, entitled “Modeling Joint Lives within Families”
Family history is usually seen as a significant factor insurance companies look at when applying for a life insurance policy. Where it is used, family history of cardiovascular diseases, death by cancer, or family history of high blood pressure and diabetes could result in higher premiums or no coverage at all. In this article, we use massive (historical) data to study dependencies between life length within families. If joint life contracts (between a husband and a wife) have been long studied in actuarial literature, little is known about child and parents dependencies. We illustrate those dependencies using 19th century family trees in France, and quantify implications in annuities computations. For parents and children, we observe a modest but significant positive association between life lengths. It yields different estimates for remaining life expectancy, present values of annuities, or whole life insurance guarantee, given information about the parents (such as the number of parents alive). A similar but weaker pattern is observed when using information on grandparents.
The paper is online on https://arxiv.org/abs/2006.08446.