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Risks
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Financial Risk, Actuarial Science, and Applications of AI Techniques
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Financial Risk, Actuarial Science, and Applications of AI Techniques

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5639

Special Issue Editors

Prof. Dr. Don Hong

E-Mail Website
Guest Editor
Mathematical Sciences, Middle Tennessee State University, Box 34, Murfreesboro, TN 37132-0001, USA
Interests: Actuarial Science and Financial Mathematics; Approximation Theory and Computational Mathematics; Splines and Wavelets with Applications; Biomedical Data Analysis and Bioinformatics; Statistical Methods and Com- puting with Applications
Dr. Tianxiang Shi

E-Mail Website
Guest Editor
Fox School of Business, Temple University, Philadelphia, PA 19122, USA
Interests: risk and insolvency analysis; pension/retirement risk management; and reverse mortgages
Prof. Dr. Chris Groendyke

E-Mail Website
Guest Editor
Department of Mathematics, Robert Morris University, Moon Township, PA 15108, USA
Interests: actuarial science and Life Insurance; asset/liability management; project management; and product management.; dynamics of disease spread through populations; methods for using epidemic data to perform statistical inference on network models; biological epidemics; contagions within financial networks; statistical inference; Bayesian approach and Markov Chain Monte Carlo methods

Special Issue Information

Dear Colleagues,

You are cordially invited to submit your research papers for the forthcoming Special Issue on financial risk and actuarial mathematics of the journal Risks, an international, scholarly, peer-reviewed, open access journal for research and studies on insurance and financial risk management.

The rapid growth in artificial intelligence (AI), associated with data science and advances in statistical computing, is transforming predictive analytics and applications, including actuarial and financial risk modeling, insurance pricing, and loss reserving, among many predictive analytics in business and risk management. This proposed Special Issue on financial risk and actuarial mathematics aims to provide a platform for authors to explore, analyze, and develop, as well as to discuss current and innovative models for predictive analytics to meet the needs of the increasing use of artificial intelligence (AI), statistical computing techniques, and data science technology in the fields of actuarial science, financial risk management, and insurance, among other related fields.

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass the pre-check are peer-reviewed. Accepted papers will be published in the journal (as soon as they are accepted) and will be listed together on the Special Issue website. Research articles, review articles, and short communications are welcome. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for an announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for the submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The article processing charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions. The deadline for submissions of full papers is May 31, 2025.

Prof. Don Hong, Ph.D.
Dr. Tianxiang Shi, Ph.D., FSA
Prof. Chris Groendyke, Ph. D., FSA
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • actuarial modeling
  • AI applications
  • capital structure
  • data-driven models
  • financial risk modeling
  • extreme events and insurance risk
  • insurance economics
  • loss reserving
  • machine learning
  • mathematical finance
  • reinsurance
  • risk management
  • mortality/longevity risk
  • pension risk analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

47 pages, 1120 KB  
Article
Model Misspecification and Data-Driven Model Ranking Approach for Insurance Loss and Claims Data
by Suparna Basu and Hon Keung Tony Ng
Risks 2025, 13(12), 231; https://doi.org/10.3390/risks13120231 - 28 Nov 2025
Viewed by 164
Abstract
Statistical models are crucial in analyzing insurance loss and claims data, offering insights into various risk elements. The prevailing statistical notion that “all models are wrong, but some are useful” can wield significant influence, particularly when multiple competing statistical models are considered. This [...] Read more.
Statistical models are crucial in analyzing insurance loss and claims data, offering insights into various risk elements. The prevailing statistical notion that “all models are wrong, but some are useful” can wield significant influence, particularly when multiple competing statistical models are considered. This becomes particularly pertinent when all models portray similar characteristics within specific subsets of the support of the random variable under scrutiny. Since the actual model is unknown in practical scenarios, the challenge of model selection becomes daunting, complicating the study of associated characteristics of the actual data generation process. To address these challenges, the concept of model averaging is embraced. Often, averaging over multiple models helps alleviate the risk of model misspecification, as different models may capture distinct aspects of the data or modeling assumptions. This enhances the robustness of the estimation process, yielding a more accurate and reasonable estimate compared to relying solely on a single model. This paper introduces two novel data-based model selection methods—one using the likelihood function and the other using the density power divergence measure. The study focuses on estimating the Value-at-Risk (VaR) for non-life insurance claim size data, providing comprehensive insights into potential losses for insurers. The performance of the proposed procedures is evaluated through Monte Carlo simulations under both uncontaminated conditions and in the presence of data contamination. Additionally, the applicability of the methods is illustrated using two real non-life insurance datasets, with the VaR values estimated at different confidence levels. Full article
(This article belongs to the Special Issue Financial Risk, Actuarial Science, and Applications of AI Techniques)
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14 pages, 362 KB  
Article
Optimizing Moral Hazard Management in Health Insurance Through Mathematical Modeling of Quasi-Arbitrage
by Lianlian Zhou, Anshui Li and Jue Lu
Risks 2025, 13(5), 84; https://doi.org/10.3390/risks13050084 - 28 Apr 2025
Viewed by 1813
Abstract
Moral hazard in health insurance arises when insured individuals are incentivized to over-utilize healthcare services, especially when they face low out-of-pocket costs. While existing literature primarily addresses moral hazard through qualitative studies, this paper introduces a quantitative approach by developing a mathematical model [...] Read more.
Moral hazard in health insurance arises when insured individuals are incentivized to over-utilize healthcare services, especially when they face low out-of-pocket costs. While existing literature primarily addresses moral hazard through qualitative studies, this paper introduces a quantitative approach by developing a mathematical model based on quasi-arbitrage conditions. The model optimizes health insurance design, focusing on the transition from Low-Deductible Health Plans (LDHPs) to High-Deductible Health Plans (HDHPs), and seeks to mitigate moral hazard by aligning the interests of both insurers and insured. Our analysis demonstrates how setting appropriate deductible levels and offering targeted premium reductions can encourage insured to adopt HDHPs while maintaining insurer profitability. The findings contribute to the theoretical framework of moral hazard mitigation in health insurance and offer actionable insights for policy design. Full article
(This article belongs to the Special Issue Financial Risk, Actuarial Science, and Applications of AI Techniques)
24 pages, 1066 KB  
Article
Interest Rate Sensitivity of Callable Bonds and Higher-Order Approximations
by Scott S. Dow and Stefanos C. Orfanos
Risks 2025, 13(4), 69; https://doi.org/10.3390/risks13040069 - 1 Apr 2025
Viewed by 2666
Abstract
Certain fixed-income securities, such as callable bonds and mortgage-backed securities subject to prepayment, typically exhibit negative convexity at low yields and cannot be adequately immunized through duration and convexity-matching alone. To address this residual risk, we examine the concepts of bond tilt and [...] Read more.
Certain fixed-income securities, such as callable bonds and mortgage-backed securities subject to prepayment, typically exhibit negative convexity at low yields and cannot be adequately immunized through duration and convexity-matching alone. To address this residual risk, we examine the concepts of bond tilt and bond agility. We provide explicit calculations and derive several approximation formulas that incorporate higher-order terms. With the help of these methods, we are able to track the price-yield dynamics of callable bonds remarkably well, achieving mean absolute errors below 2.5% across a wide variety of callable bonds for parallel yield shifts of up to ±200 basis points. Full article
(This article belongs to the Special Issue Financial Risk, Actuarial Science, and Applications of AI Techniques)
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