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Risks
Special Issues
Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance
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Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 5291

Special Issue Editors

Dr. Georgios Pitselis

E-Mail Website
Guest Editor
Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece
Interests: credibility premium estimation; robust estimation; ratemaking and reserving; solvency ii; actuarial risk management; non-life risks; modelling mortality and longevity risk; econometric models for insurance
Special Issues, Collections and Topics in MDPI journals
Dr. Konstadinos Politis

E-Mail Website
Guest Editor
Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece
Interests: stochastic processes; actuarial risk theory; insurance mathematics
Special Issues, Collections and Topics in MDPI journals
Dr. Apostolos Bozikas

E-Mail Website
Guest Editor
Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece
Interests: credibility theory; stochastic mortality modelling; reserving; securitization of longevity risk; actuarial pension plans; actuarial risk management; life and health insurance pricing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to inform you that we are guest editing a Special Issue entitled “Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance”, which will be published in Risks (https://www.mdpi.com/journal/risks, ISSN 2227-9091). This Special Issue is now open to receive submissions of full research articles and comprehensive review papers for peer-review.

Quantitative risk management (QRM) is one of the most challenging tasks for financial institutions, such as banks, insurance companies, etc. As financial products are becoming more complex, new risk management methods have to be adjusted to these new products. QRM incorporates a wide range of techniques from different disciplines (including statistics, mathematics, and finance) to address issues related to the operation and the regulation of financial institutions.

This Special Issue aims to highlight the interplay between statistical and probabilistic aspects, on the one hand, and the risk management process on the other. We thus welcome submissions of high-quality articles that present recent developments or introduce new theoretical (or practical) advances in the areas of statistics, stochastic modelling and quantitative risk management with applications related to the insurance industry.

Topics of interest for this Special Issue include, but are not limited to, the following:
- Catastrophe risk management
- Computational methods for insurance pricing
- Cyber insurance and risk management
- Econometric models for risk management
- Estimation and evaluation of risk management models
- Extreme value theory in risk management
- Insurance risk management
- Longevity/mortality modelling and risk management
- Loss distributions and their applications in insurance risk management
- Solvency for financial institutions and risk aggregation

This Special Issue is a continuation of the previous successful Special Issue “Statistics and Quantitative Risk Management for Insurance”.

Dr. Georgios Pitselis
Dr. Konstadinos Politis
Dr. Apostolos Bozikas
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 science
  • cyber risk management
  • dependence modelling
  • extreme events
  • finance
  • insurance
  • longevity risk
  • multivariate analysis
  • quantitative risk management
  • risk analysis
  • risk management
  • risk measures
  • statistical methods
  • stochastic modelling

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

Published Papers (3 papers)

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Research

22 pages, 6143 KiB  
Article
Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance
by Shengkun Xie and Nathaniel Ho
Risks 2024, 12(7), 108; https://doi.org/10.3390/risks12070108 - 1 Jul 2024
Viewed by 875
Abstract
This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using [...] Read more.
This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting years that summarize multiple accident year loss metrics. By integrating spatial clustering techniques, the study not only improves the credibility of predictive models but also introduces a strategic dimension reduction method that concurrently enhances the interpretability of predictive models used. The evolving nature of spatial patterns over time poses a significant barrier to a better understanding of complex insurance systems as these patterns transform due to various factors. While spatial clustering effectively identifies regions with similar loss data characteristics, maintaining up-to-date clusters is an ongoing challenge. This research underscores the importance of studying spatial patterns of auto insurance claim data across major insurance coverage types, including Accident Benefits (AB), Collision (CL), and Third-Party Liability (TPL). The research offers regulators valuable insights into distinct risk profiles associated with different coverage categories and territories. By leveraging spatial loss data from pre-pandemic and pandemic periods, this study also aims to uncover the impact of the COVID-19 pandemic on auto insurance claims of major coverage types. From this perspective, we observe a statistically significant increase in insurance premiums for CL coverage after the pandemic. The proposed unified spatial clustering method incorporates a relabeling strategy to standardize comparisons across different accident years, contributing to a more robust understanding of the pandemic effects on auto insurance claims. This innovative approach has the potential to significantly influence data visualization and pattern recognition, thereby improving the reliability and interpretability of clustering methods. Full article
(This article belongs to the Special Issue Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance)
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29 pages, 610 KiB  
Article
Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data
by Claudio Mazzi, Angelo Damone, Andrea Vandelli, Gastone Ciuti and Milena Vainieri
Risks 2024, 12(2), 24; https://doi.org/10.3390/risks12020024 - 29 Jan 2024
Cited by 2 | Viewed by 1927
Abstract
One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods [...] Read more.
One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods for estimating reserves inadequate. We propose a new methodology to estimate claim reserves in the healthcare insurance system based on generalized linear models using the Overdispersed Poisson distribution function. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss–Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss–Newton method, where the choice of the initial guess is fundamental. This methodology is applied as a case study to the healthcare system of the Tuscany region. The results were validated by comparing them with state-of-the-art measurement of the confidence intervals of the Overdispersed Poisson distribution parameters with better outcomes. Hence, local healthcare authorities could use the proposed and improved methodology to allocate resources dedicated to healthcare and global management. Full article
(This article belongs to the Special Issue Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance)
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27 pages, 957 KiB  
Article
Credibility Distribution Estimation with Weighted or Grouped Observations
by Georgios Pitselis
Risks 2024, 12(1), 10; https://doi.org/10.3390/risks12010010 - 3 Jan 2024
Viewed by 1808
Abstract
In non-life insurance practice, actuaries are often faced with the challenge of predicting the number of claims and claim amounts to be incurred at any given time, which serve to implement fair pricing and reserves given the nature of the risk. This paper [...] Read more.
In non-life insurance practice, actuaries are often faced with the challenge of predicting the number of claims and claim amounts to be incurred at any given time, which serve to implement fair pricing and reserves given the nature of the risk. This paper extends Jewell’s credible distribution in terms of forecasting the distribution of individual risk in cases where the observations are weighted or are grouped in intervals. More specifically, we show how empirical distribution functions can be embedded within Bühlmann’s and Straub’s credibility model. The optimal projection theorem is applied for credibility estimation and more insight into the derivation of the credibility distribution estimators is also provided. In addition, distribution credibility estimators are established and numerical illustrations are presented herein. Two examples of distribution credibility estimation are given, one with insurance loss data and the other with industry financial data. Full article
(This article belongs to the Special Issue Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance)
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