Yesterday, Jonno White published a “a list of 50 actuarial thought leaders who genuinely deserve to be far better known outside the profession itself“. I have to admit that I am proud, honoured (and a little surprised) to be included in this list, alongside colleagues whose work I deeply admire (such as Moshe Milevsky, David Blake, Andrew Cairns, Johnny Siu-hang Li, Gordon Woo, Ron Richman, Ioannis Kyriakou, Paul Embrechts, Jan Dhaene, Michel Denuit, Hansjörg Albrecher, Runhuan Feng, Steve Haberman, Mary Hardy, Joey Zhou, Andrés Villegas, Jennifer Wang, Les Mayhew…)
Arthur Charpentier is a Professor at UQAM in Montreal and one of the most prolific researchers working at the boundary of actuarial science and data science. He is the editor and primary author of Computational Actuarial Science with R, one of the most widely used modern actuarial methodology texts, and he has published extensively on credibility theory, copula models, non-life insurance pricing, and the application of machine learning to actuarial problems.
His blog and public writing on actuarial methodology have built an audience far beyond the academic actuarial community, reaching data scientists and statisticians who encounter actuarial problems in insurance and risk management without having actuarial training. That crossover audience is precisely where the most interesting methodological conversations are happening as the boundaries between actuarial science, statistics, and machine learning continue to blur.
OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (June 26, 2026). “50 actuarial thought leaders…” Freakonometrics. Retrieved September 12, 2026 from https://doi.org/10.58079/16nj8