Statistical Models for Insurance

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 477

Special Issue Editor


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Guest Editor
Department of Statistics and Actuarial Science, The University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: data-driven statistical methods; insurance; machine learning algorithms; fields including medicine and public health

Special Issue Information

Dear Colleagues,

This Special Issue aims to develop statistical models tailored to the unique demands of the insurance industry. The scope of the Special Issue extends to various types of insurance, such as life, health, property, and casualty, addressing challenges related to claim frequency, severity, and the estimation of risk premiums. By integrating advanced statistical techniques with domain-specific knowledge, this Special Issue seeks to provide statistical tools for actuaries and insurance professionals to better manage uncertainty, optimize pricing strategies, and improve decision-making processes in the face of evolving risks and regulatory environments. We invite papers presenting original research on related topics including, but not limited to, the following:

  • Advanced Techniques in Mortality Modeling and Forecasting
  • Modeling and Pricing Strategies for Agricultural Insurance
  • Risk Assessment and Pricing in Property and Casualty Insurance
  • Multi-Line Insurance Dependence Modeling
  • Fraud Detection Algorithms in Insurance
  • Analyzing Telematics Data for Personalized Insurance
  • Geospatial Risk Assessment and Insurance Applications
  • Climate Change Impact Models and Their Insurance Implications
  • Leveraging Text Mining for Enhanced Claims Processing

Dr. Liqun Diao
Guest Editor

Manuscript Submission Information

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Keywords

  • modeling and forecasting
  • risk management
  • statistical models
  • data mining

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Published Papers (1 paper)

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Research

28 pages, 527 KiB  
Article
A Multistate Analysis of Policyholder Behaviour in Life Insurance—Lasso-Based Modelling Approaches
by Lucas Reck, Johannes Schupp and Andreas Reuß
Risks 2025, 13(4), 73; https://doi.org/10.3390/risks13040073 - 9 Apr 2025
Viewed by 273
Abstract
Holders of life insurance policies can exercise various options that lead to contract modifications, e.g., full surrender, partial surrender, and paid-up and dynamic premium increase options. Transitions between these contract states materially affect (current and future) cash flows and thus represent a serious [...] Read more.
Holders of life insurance policies can exercise various options that lead to contract modifications, e.g., full surrender, partial surrender, and paid-up and dynamic premium increase options. Transitions between these contract states materially affect (current and future) cash flows and thus represent a serious source of uncertainty for an insurance company. It is common practice to determine best-estimate assumptions for these transitions independently, i.e., without considering joint determinants of the different aspects of policyholder behaviour. The recent literature also incorporates multistate classical statistical models. Our paper shows how consistent best-estimate transition rates for multiple status transitions can be derived using data science methods. More specifically, we extend existing multivariate approaches based on established statistical models (generalised linear models) with the Lasso method, such that the key drivers for each transition can be identified automatically. We discuss the performance, the complexity and the practical applicability of the different modelling approaches based on data from a European insurer. Full article
(This article belongs to the Special Issue Statistical Models for Insurance)
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