Financial Risk, Actuarial Science, and Applications of AI Techniques

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 950

Special Issue Editors


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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

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Guest Editor
Fox School of Business, Temple University, Philadelphia, PA 19122, USA
Interests: risk and insolvency analysis; pension/retirement risk management; and reverse mortgages

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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 100 words) can be sent to the Editorial Office for 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 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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Published Papers (2 papers)

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Research

14 pages, 362 KiB  
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 (registering DOI) - 28 Apr 2025
Viewed by 92
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 KiB  
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 232
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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