UNSW Actuarial Software Directory
School of Risk and Actuarial Studies
This directory is a collation of the actuarial software packages and research papers with code that have been developed by UNSW academics. Some educational resources are also linked below.
Software Packages
AffineMortality
AffineMortality implements univariate Kalman Filter-based routines for parameter estimation, projection, and analysis of affine mortality models. See Ungolo et al. (2023) for the methodology, and Ungolo et al. (2024) for details on the usage.
clmplus
clmplus is a toolbox of Chain Ladder Plus models. It implements the age-period-cohort models for the claim development presented in Pittarello et al. (2023).
DRN
DRN (Distributional Refinement Network) is a deep learning model for distributional forecasting developed in Python.
iMoMo
iMoMo (Mortality Improvement Rate Modelling) is an extension of the StMoMo package to allow the modelling of mortality improvement rates under a Poisson setting.
STLT
STLT fits the Smooth Threshold Life Table (STLT) and Dynamic Smooth Threshold Life Table (DSTLT) as outlined in Huang et al. (2020). It also provides S3 methods for predicting using fitted STLT and DSTLT models, as well as plotting the fitted lines.
StMoMo
StMoMo (Stochastic Mortality Modelling) is an R package providing functions to specify and fit stochastic mortality models including the Lee-Carter models, the CBD model, and the APC model.
Papers with Code
Distributional Refinement Network: Distributional Forecasting via Deep Learning
Avanzi, B., Dong, E., Laub, P., & Wong, B. (2026)
Impact or Responsibility? Giving Behaviour in a Televised Natural Experiment
Hiabu, I. E., Smeets, P., van den Assem, M. J., & van Dolder, D. (2026)
Replicating and Extending Chain-Ladder via an Age–Period–Cohort Structure on the Claim Development in a Run-Off Triangle
Pittarello, G., Hiabu, M., & Villegas, A. M. (2026)
Cohort trends in intrinsic capacity in England and China
Beard, J. R., Hanewald, K., Si, Y., Amuthavalli Thiyagarajan, J., & Moreno-Agostino, D. (2025)
Hawkes Models and Their Applications
Laub, P. J., Lee, Y., Pollett, P. K., & Taimre, T. (2025)
Actuarial modelling of Australian population retirement risks: an Australian functional disability and health state model
Park, K., & Sherris, M. (2025)
AffineMortality: An R package for estimation, analysis, and projection of affine mortality models
Ungolo, F., Garces, L. P. D. M., Sherris, M., & Zhou, Y. (2025)
Detection and treatment of outliers for multivariate robust loss reserving
Avanzi, B., Lavender, M., Taylor, G., & Wong, B. (2024)
On the impact of outliers in loss reserving
Avanzi, B., Lavender, M., Taylor, G., & Wong, B. (2024)
Ensemble distributional forecasting for insurance loss reserving
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2024)
Machine Learning with High-Cardinality Categorical Features in Actuarial Applications
Avanzi, B., Taylor, G., Wang, M., & Wong, B. (2024)
A neural network approach for the mortality analysis of multiple populations: a case study on data of the Italian population
Euthum, M., Scherer, M., & Ungolo, F. (2024)
Estimation, Comparison, and Projection of Multifactor Age–Cohort Affine Mortality Models
Ungolo, F., Garces, L. P. D. M., Sherris, M., & Zhou, Y. (2024)
An Augmented Variable Dirichlet Process mixture model for the analysis of dependent lifetimes
Ungolo, F., & Laub, P. (2024)
A Dirichlet process mixture regression model for the analysis of competing risk events
Ungolo, F., & van den Heuvel, E. R. (2024)
Zero-knowledge proofs in education: a pathway to disability inclusion and equitable learning opportunities
Xu, X. (2024)
Finite-time ruin probabilities using bivariate Laguerre series
Cheung, E. C. K., Lau, H., Willmot, G. E., & Woo, J. K. (2023)
Cause-of-death mortality forecasting using adaptive penalized tensor decompositions
Zhang, X., Huang, F., Hui, F. K. C., & Haberman, S. (2023)
A Group Regularisation Approach for Constructing Generalised Age-Period-Cohort Mortality Projection Models
Sridaran, D., Sherris, M., Villegas, A. M., & Ziveyi, J. (2022)
Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework
Avanzi, B., Taylor, G., Wong, B., & Xian, A. (2021)
Mortality Improvement Rates: Modeling, Parameter Uncertainty, and Robustness
Hunt, A., & Villegas, A. M. (2021)
Modelling life tables with advanced ages: An extreme value theory approach
Huang, F., Maller, R., & Ning, X. (2020)
A hierarchical model for the joint mortality analysis of pension scheme data with missing covariates
Ungolo, F., Kleinow, T., & Macdonald, A. S. (2020)
Survival analysis of pension scheme mortality when data are missing
Ungolo, F., Christiansen, M. C., Kleinow, T., & MacDonald, A. S. (2019)
Flood risk management and adaptation under sea level rise uncertainty
Truong, C., Li, H., Trück, S., Malavasi, M. (2022)
Who are we?
The UNSW School of Risk and Actuarial Studies is globally recognized as a leader in actuarial science, with research and education spanning three primary areas: Risk (quantitative risk management, mortality, longevity and health risks, insurance risk modeling, climate change, AI/ML-enhanced actuarial analytics, uncertainty); Insurance (general insurance, life insurance, pricing, capital, and reserving); and Superannuation (pension economics, retirement products, and behavioral insights).