Machine Learning and Statistical Learning in Insurance and Actuarial Science

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 2445

Special Issue Editors

Department of Risk and Insurance, School of Business, University of Wisconsin-Madison, Madison, WI 53597, USA
Interests: dependence models; insurance analytics; actuarial data science
Department of Economics, University of Melbourne, Melbourne, VIC 3010, Australia
Interests: discrete-time risk models; correlations; bonus-malus system; actuarial statistics; general insurance modelling; rainfall/flood modelling; health care & ageing

Special Issue Information

Dear Colleagues,

In the last decade, machine learning and statistical learning have seen vast applications in the insurance field, both in practice and in research. In the insurance industry, Artificial Intelligence has begun to play a key role in reshaping insurance claims, distribution, and underwriting and pricing. Advancements in machine learning and statistical learning enable actuarial researchers to develop better data-driven methods on studying real-life insurance-related problems.

Since early 2020, there have been dramatic changes in people’s lives all over the world due to the COVID-19 pandemic and climate change. Lengthy lockdowns, extreme weather conditions, and the COVID-19 pandemic continued to affect world, impacting every aspect of individuals’ life.

This Special Issue aims to showcase innovative applications of most recent machine learning and statistical learning developments in post-COVID-19 insurance practice and research, in particular, on insurance automation, InsurTech, extreme weather forecasting, and health and aging. We welcome contributions in areas including, but not limited to, the topics listed below (in alphabetical order):

Dr. Peng Shi
Dr. Xueyuan Wu
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

  • artificial intelligence;
  • COVID-19 pandemic and insurance;
  • extreme weather condition forecasting;
  • insurance technology;
  • machine learning and deep learning;
  • insurance innovation and disruption;
  • discrimination and algorithm bias;
  • cyber risk;
  • analytics in insurance markets and operations

Published Papers (1 paper)

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Research

23 pages, 857 KiB  
Article
A Generalized Linear Mixed Model for Data Breaches and Its Application in Cyber Insurance
by Meng Sun and Yi Lu
Risks 2022, 10(12), 224; https://doi.org/10.3390/risks10120224 - 23 Nov 2022
Cited by 2 | Viewed by 1664
Abstract
Data breach incidents result in severe financial loss and reputational damage, which raises the importance of using insurance to manage and mitigate cyber related risks. We analyze data breach chronology collected by Privacy Rights Clearinghouse (PRC) since 2001 and propose a Bayesian generalized [...] Read more.
Data breach incidents result in severe financial loss and reputational damage, which raises the importance of using insurance to manage and mitigate cyber related risks. We analyze data breach chronology collected by Privacy Rights Clearinghouse (PRC) since 2001 and propose a Bayesian generalized linear mixed model for data breach incidents. Our model captures the dependency between frequency and severity of cyber losses and the behavior of cyber attacks on entities across time. Risk characteristics such as types of breach, types of organization, entity locations in chronology, as well as time trend effects are taken into consideration when investigating breach frequencies. Estimations of model parameters are presented under Bayesian framework using a combination of Gibbs sampler and Metropolis–Hastings algorithm. Predictions and implications of the proposed model in enterprise risk management and cyber insurance rate filing are discussed and illustrated. We find that it is feasible and effective to use our proposed NB-GLMM for analyzing the number of data breach incidents with uniquely identified risk factors. Our results show that both geological location and business type play significant roles in measuring cyber risks. The outcomes of our predictive analytics can be utilized by insurers to price their cyber insurance products, and by corporate information technology (IT) and data security officers to develop risk mitigation strategies according to company’s characteristics. Full article
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