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Risks
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Statistical Applications to Insurance and Risk
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Statistical Applications to Insurance and Risk

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 2864

Special Issue Editor

Dr. Silvia Dedu

E-Mail Website
Guest Editor
Department of Applied Mathematics, Bucharest University of Economic Studies, 6 Romana Sq., District 1, 010734 Bucharest, Romania
Interests: statistics; risk theory; information theory; operations research; risk measures; entropy measures; actuarial science; financial mathematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Welcome to the Special Issue on "Statistical Applications to Insurance and Risk". This Special Issue aims to explore innovative statistical methodologies and their applications in the insurance and risk management domain. Insurance and risk assessment are integral components of modern financial and business strategies. They play a crucial role in safeguarding against unforeseen events and uncertainties.

This Special Issue seeks to provide a platform for researchers, practitioners, and experts in statistics and insurance to showcase their cutting-edge work. We invite contributions that highlight novel statistical approaches, models, and tools that address various aspects of insurance and risk analysis. Topics of interest include, but are not limited to:

Actuarial Science: statistical methods for premium pricing, reserving, and loss modeling.

Risk Management: advanced statistical techniques for risk assessment and mitigation.

Data Analytics: big data analytics and machine learning in insurance applications.

Extreme Value Theory: statistical modeling of rare and extreme events.

Fraud Detection: statistical methods for detecting insurance fraud.

Catastrophe Modeling: statistical approaches to assess and manage catastrophic risks.

Health and Life Insurance: statistical modeling in health and life insurance contexts.

Cyber Insurance: statistical analysis of cybersecurity risks.

Climate and Environmental Risks: statistical methods for climate-related and environmental risk assessment.

We encourage submissions that promote interdisciplinary collaboration between statisticians, actuaries, economists, and experts in the insurance and risk management industry. Join us in advancing the field of statistical applications in insurance and risk, contributing to a safer and more secure future.

Dr. Silvia Dedu
Guest Editor

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

  • insurtech
  • predictive modeling
  • Bayesian statistics
  • risk aggregation
  • longevity risk
  • regime-switching models
  • claims reserving
  • telematics data analysis

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Published Papers (2 papers)

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Research

18 pages, 523 KiB  
Article
Inference for the Parameters of a Zero-Inflated Poisson Predictive Model
by Min Deng, Mostafa S. Aminzadeh and Banghee So
Risks 2024, 12(7), 104; https://doi.org/10.3390/risks12070104 - 24 Jun 2024
Cited by 1 | Viewed by 718
Abstract
In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of [...] Read more.
In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed. Full article
(This article belongs to the Special Issue Statistical Applications to Insurance and Risk)
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19 pages, 512 KiB  
Article
Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm
by Robin Van Oirbeek, Félix Vandervorst, Thomas Bury, Gireg Willame, Christopher Grumiau and Tim Verdonck
Risks 2024, 12(5), 79; https://doi.org/10.3390/risks12050079 - 14 May 2024
Viewed by 1423
Abstract
Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where [...] Read more.
Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA’s effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector. Full article
(This article belongs to the Special Issue Statistical Applications to Insurance and Risk)
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