Bayes’ actuarial research world-leading, rankings suggest
New rankings confirm the value of Bayes' actuarial science research - underlining the contribution of Bayes academics to a vital but sometimes obscure profession.
Bayes Business School’s actuarial research is the best in Europe and in the global top three, new international rankings suggest.
Since 1990, the University of Nebraska-Lincoln has assessed the quality of every paper that academics from 50 universities have published in the four leading actuarial journals. The latest ranking, which covers articles published between 2021 and 2025, puts Bayes behind just two Australian institutions: the University of New South Wales Business School and Macquarie University.
The universities of Lausanne and Melbourne round out the top five.
Ioannis Kyriakou, Professor of Actuarial Finance and Director of Bayes’ MSc Actuarial Programme Cluster, said: “This achievement reflects more than just the depth and quality of the research produced at Bayes. It is testament to the consistency of our scholarly contribution since the business school launched the first of our groundbreaking actuarial science programmes more than half a century ago.
“It is especially gratifying that the ranking methodology is all about internationally recognised journal publications and collaborative academic impact. We are proud of this milestone and grateful to all our actuarial researchers whose work continues to strengthen Bayes’ reputation as a leading centre for actuarial science research internationally.
We’ve pulled together below some of the great papers that have helped secure this ranking.
They include:
Asimit, V., Chong, W.F., Tunaru, R. and Zhou, F. (2025). Portfolio selection and risk sharing via risk budgeting. Insurance: Mathematics and Economics, 125, pp. 103139-103139. doi:10.1016/j.insmatheco.2025.103139
Asimit, V., Fung, T.C., Peng, L. and Yang, F. (2025). Diversification effect in multivariate optimal risk transfer. Insurance: Mathematics and Economics, 125, pp. 103156-103156. doi:10.1016/j.insmatheco.2025.103156
Asimit, V., Badescu, A., Chen, Z. and Zhou, F. (2025). Efficient and proper generalised linear models with power link functions. Insurance: Mathematics and Economics, 122, pp. 91-118. doi:10.1016/j.insmatheco.2025.02.005
Asimit, V., Yuan, Z. and Zhou, F. (2025). Tail similarity. Insurance: Mathematics and Economics, 121, pp. 26-44. doi:10.1016/j.insmatheco.2024.12.004
Bischofberger, S.M., Hiabu, M., Mammen, E. and Nielsen, J.P. (2025). Smooth backfitting for additive hazard rates. Scandinavian Journal of Statistics, 52(4), pp. 1625-1669. doi:10.1111/sjos.70004
Yanez, J.S., Guillén, M. and Nielsen, J.P. (2025). Weekly dynamic motor insurance ratemaking with a telematics signals bonus-malus score. ASTIN Bulletin, 55(1), pp. 1-28. doi:10.1017/asb.2024.30
Atance, D., Haberman, S. and Millossovich, P. (2025). The dynamic of mortality explained with a reduced number of key ages. Scandinavian Actuarial Journal pp. 1-27. doi:10.1080/03461238.2025.2596036
Shang, H.L. and Haberman, S. (2025). Constructing prediction intervals for the age distribution of deaths. Scandinavian Actuarial Journal pp. 1-18. doi:10.1080/03461238.2025.2544265
Jeong, S.Y., Owadally, I., Haberman, S. and Wright, D. (2025). Subjective survival beliefs and the life-cycle model. Insurance: Mathematics and Economics, 122, pp. 11-29. doi:10.1016/j.insmatheco.2025.01.007
De Mori, L., Haberman, S., Millossovich, P. and Zhu, R. (2025). Mortality forecasting via multi-task neural networks. ASTIN Bulletin, 55(2), pp. 313-331. doi:10.1017/asb.2025.10
Shang, H.L. and Haberman, S. (2025). Forecasting age distribution of deaths: Cumulative distribution function transformation. Insurance: Mathematics and Economics, 122, pp. 249-261. doi:10.1016/j.insmatheco.2025.03.007
Shang, H.L. and Haberman, S. (2025). Forecasting age distribution of life-table death counts via α -transformation. Scandinavian Actuarial Journal, 2025(4), pp. 387-403. doi:10.1080/03461238.2024.2425723
Villegas, A.M., Bajekal, M., Haberman, S. and Zhou, L. (2024). Key Drivers of Long-Term Rates of Mortality Improvements in the United States: Period, Cohort, and Cause of Death Analysis, 1959–2016. North American Actuarial Journal, 28(1), pp. 187-217. doi:10.1080/10920277.2023.2167834
