UNSW Actuarial Software Directory

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.

DRN

DRN (Distributional Refinement Network) is a deep learning model for distributional forecasting developed in Python.

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).

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.

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.

Papers with Code

An Augmented Variable Dirichlet Process Mixture model for the analysis of dependent lifetimes

Ungolo, F. & Laub, P.J. (2024)

ASTIN Bulletin (to appear) Code

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)

European Actuarial Journal Code

A Dirichlet process mixture regression model for the analysis of competing risk events

Ungolo, F., van den Heuvel, E. R. (2024)

Insurance: Mathematics and Economics Code

Ensemble distributional forecasting for insurance loss reserving

Avanzi, B., Li, Y., Wong, B., & Xian, A. (2024)

Scandinavian Actuarial Journal Code

AffineMortality: An R package for estimation, analysis, and projection of affine mortality models

Ungolo, F., Garces, L. P. D. M., Sherris, M., & Zhou, Y. (2024)

Annals of Actuarial Science Code

Machine Learning with High-Cardinality Categorical Features in Actuarial Applications

Avanzi, B., Taylor, G., Wang, M., & Wong, B. (2024)

ASTIN Bulletin Code

Zero-knowledge proofs in education: a pathway to disability inclusion and equitable learning opportunities

Xu, X. (2024)

Smart Learning Environments Code

Distributional Refinement Network: Distributional Forecasting via Deep Learning

Avanzi, B., Dong, E., Laub, P. J., Wong, B. (2024)

Preprint Code

Estimation, Comparison, and Projection of Multifactor Age–Cohort Affine Mortality Models

Ungolo, F., Garces, L. P. D. M., Sherris, M., & Zhou, Y. (2024)

North American Actuarial Journal Code

On the impact of outliers in loss reserving

Avanzi, B., Lavender, M., Taylor, G., & Wong, B. (2024)

European Actuarial Journal Code

Detection and treatment of outliers for multivariate robust loss reserving

Avanzi, B., Lavender, M., Taylor, G., & Wong, B. (2024)

Annals of Actuarial Science Code

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. (2023)

Preprint Code

Finite-time ruin probabilities using bivariate Laguerre series

Cheung, E. C. K., Lau, H., Willmot, G. E., & Woo, J. K. (2023)

Scandinavian Actuarial Journal Code

Cause-of-death mortality forecasting using adaptive penalized tensor decompositions

Zhang, X., Huang, F., Hui, F. K. C., & Haberman, S. (2023)

Insurance: Mathematics and Economics Code

A Group Regularisation Approach for Constructing Generalised Age-Period-Cohort Mortality Projection Models

Sridaran, D., Sherris, M., Villegas, A. M., & Ziveyi, J. (2022)

ASTIN Bulletin Code

Flood risk management and adaptation under sea level rise uncertainty

Truong, C., Li, H., Trück, S., Malavasi, M. (2022)

Society of Actuaries Report Code

Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework

Avanzi, B., Taylor, G., Wong, B., & Xian, A. (2021)

European Journal of Operational Research Code

Mortality Improvement Rates: Modeling, Parameter Uncertainty, and Robustness

Hunt, A., & Villegas, A. M. (2021)

North American Actuarial Journal Code

A hierarchical model for the joint mortality analysis of pension scheme data with missing covariates

Ungolo, F., Kleinow, T., & Macdonald, A. S. (2020)

Insurance: Mathematics and Economics Code

Modelling life tables with advanced ages: An extreme value theory approach

Huang, F., Maller, R., & Ning, X. (2020)

Insurance: Mathematics and Economics Code

Survival analysis of pension scheme mortality when data are missing

Ungolo, F., Christiansen, M. C., Kleinow, T., & MacDonald, A. S. (2019)

Scandinavian Actuarial Journal Code

Related Resources

The course websites for some of our actuarial data science courses are listed below:

ACTL3142 Statistical Machine Learning for Risk and Actuarial Applications
ACTL3143 Artificial Intelligence and Deep Learning Models for Actuarial Applications
ACTL4305 Actuarial Data Science Applications (to be added in late 2025)

Other resources include:

Actuaries Institute Actuaries’ Analytical Cookbook

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).