Actuarial Statistical Modeling and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1028

Special Issue Editor


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Guest Editor
Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
Interests: actuarial science; predictive modeling; general insurance

Special Issue Information

Dear Colleagues,

The field of actuarial science has seen rapid advancements due to the increasing complexity of risk assessment and management across various sectors, including insurance, finance, and healthcare. As data become more abundant and sophisticated statistical methods evolve, there is a growing need to integrate innovative modeling techniques to address emerging challenges.

Recent trends indicate a shift towards more data-driven approaches, with an emphasis on machine learning and artificial intelligence. However, actuaries face significant challenges, including the need for robust validation methods, regulatory compliance, and the ethical implications of algorithmic decision making. This Special Issue will provide a platform for discussing these challenges and exploring solutions that ensure the integrity and reliability of actuarial models.

By fostering a dialog around innovative methodologies and the practical implications of actuarial statistical modeling, this Special Issue seeks to advance the field and equip practitioners with the tools necessary to navigate an increasingly complex risk landscape. We invite researchers and practitioners to contribute their insights and findings to enrich this vital discourse. This Special Issue is now open to receive submissions of full research articles and comprehensive review papers for peer review from diverse topics, which may include but are not limited to the following:

  • Survival analysis;
  • Longitudinal data analysis;
  • Multivariate data analysis;
  • Loss models;
  • Integration of big data in actuarial practices;
  • Portfolio optimization;
  • Extreme value theory;
  • Fraud detection in insurance.

Dr. Himchan Jeong
Guest Editor

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Keywords

  • predictive analytics
  • statistical learning
  • dependence modeling
  • quantitative risk management
  • actuarial pricing and reserving
  • risk classification
  • survival analysis
  • longitudinal data analysis
  • multivariate data analysis
  • portfolio optimization
  • extreme value theory
  • actuarial science
  • predictive modeling
  • actuarial statistics

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Published Papers (2 papers)

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Research

22 pages, 1175 KiB  
Article
ResPoNet: A Residual Neural Network for Efficient Valuation of Large Variable Annuity Portfolios
by Heng Xiong, Jie Xu, Rogemar Mamon and Yixing Zhao
Mathematics 2025, 13(12), 1916; https://doi.org/10.3390/math13121916 - 8 Jun 2025
Abstract
Accurately valuing large portfolios of Variable Annuities (VAs) poses a significant challenge due to the high computational burden of Monte Carlo simulations and the limitations of spatial interpolation methods that rely on manually defined distance metrics. We introduce a residual portfolio valuation network [...] Read more.
Accurately valuing large portfolios of Variable Annuities (VAs) poses a significant challenge due to the high computational burden of Monte Carlo simulations and the limitations of spatial interpolation methods that rely on manually defined distance metrics. We introduce a residual portfolio valuation network (ResPoNet), a novel residual neural network architecture enhanced with weighted loss functions, designed to improve valuation accuracy and scalability. ResPoNet systematically accounts for mortality risk and path-dependent liabilities using residual layers, while the custom loss function ensures better convergence and interpretability. Numerical results on synthetic portfolios of 100,000 contracts show that ResPoNet achieves significantly lower valuation errors than baseline neural and spatial methods, with faster convergence and improved generalization. Sensitivity analysis reveals key drivers of performance, including guarantee complexity and contract maturity, demonstrating the robustness and practical applicability of ResPoNet in large-scale VA valuation. Full article
(This article belongs to the Special Issue Actuarial Statistical Modeling and Applications)
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26 pages, 621 KiB  
Article
A Bivariate Extension of Type-II Generalized Crack Distribution for Modeling Heavy-Tailed Losses
by Taehan Bae and Hanson Quarshie
Mathematics 2024, 12(23), 3718; https://doi.org/10.3390/math12233718 - 27 Nov 2024
Viewed by 612
Abstract
As an extension of the (univariate) Birnbaum–Saunders distribution, the Type-II generalized crack (GCR2) distribution, built on an appropriate base density, provides a sufficient level of flexibility to fit various distributional shapes, including heavy-tailed ones. In this paper, we develop a bivariate extension of [...] Read more.
As an extension of the (univariate) Birnbaum–Saunders distribution, the Type-II generalized crack (GCR2) distribution, built on an appropriate base density, provides a sufficient level of flexibility to fit various distributional shapes, including heavy-tailed ones. In this paper, we develop a bivariate extension of the Type-II generalized crack distribution and study its dependency structure. For practical applications, three specific distributions, GCR2-Generalized Gaussian, GCR2-Student’s t, and GCR2-Logistic, are considered for marginals. The expectation-maximization algorithm is implemented to estimate the parameters in the bivariate GCR2 models. The model fitting results on a catastrophic loss dataset show that the bivariate GCR2 distribution based on the generalized Gaussian density fits the data significantly better than other alternative models, such as the bivariate lognormal distribution and some Archimedean copula models with lognormal or Pareto marginals. Full article
(This article belongs to the Special Issue Actuarial Statistical Modeling and Applications)
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