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Risks
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Innovations in Non-Life Insurance Pricing and Reserving
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Innovations in Non-Life Insurance Pricing and Reserving

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1861

Special Issue Editors

Prof. Dr. Hua Chen

E-Mail Website
Guest Editor
College of Business, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, USA
Interests: financial stability and systemic risk; enterprise/corporate risk management; longevity risk management; insurance economics
Dr. Liang Yang

E-Mail Website
Guest Editor
School of Finance, Southwestern University of Finance and Economics, Chengdu 611130, China
Interests: non-life actuarial insurance; auto pricing; catastrophe insurance; risk management and assessment; reserve assessment; premium rate determination; application of machine learning methods on big data of vehicle network

Special Issue Information

Dear Colleagues, 

The non-life insurance industry is experiencing profound changes fueled by climate change, technological advancements, and shifting risk dynamics. Innovations in machine learning, IoT ecosystems, and parametric risk transfer are reshaping traditional actuarial methods. Meanwhile, events such as COVID-19 and the increasing frequency of natural disasters highlight the pressing need to close protection gaps in an evolving risk environment. At the same time, the rapid growth of InsurTech solutions calls for innovative strategies with which to harmonize technological progress with regulatory compliance.

This Special Issue seeks to bring together pioneering research on methodological advancements and practical applications in non-life insurance risk modeling, pricing, reserving, and alternative risk transfer strategies. We particularly recognize the need to integrate climate science, real-time data analytics, and decentralized technologies into actuarial frameworks. Submissions should address, but are not limited to, the following themes: 

  • Digital transformation and InsurTech.
  • Climate change and parametric insurance.
  • Cyber insurance and emerging risks.
  • Green insurance products and ESG insurance regulation.
  • Innovations in catastrophe risk modeling/pricing.
  • Peer-to-peer (P2P) insurance and mutual risk sharing.
  • Innovations in auto insurance pricing and reserving.

We look forward to your valuable contributions to this Special Issue.

Prof. Dr. Hua Chen
Dr. Liang Yang
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 250 words) can be sent to the Editorial Office for assessment.

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

  • non-life insurance risk modeling, pricing and reserving
  • insurance risks
  • InsurTech
  • alternative risk
  • cyber insurance
  • auto insurance
  • parametric insurance
  • green insurance products
  • ESG insurance
  • catastrophe risk modeling/pricing
  • P2P insurance

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Published Papers (1 paper)

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Research

30 pages, 1354 KB  
Article
Driving Behavior and Insurance Pricing: A Framework for Analysis and Some Evidence from Italian Data Using Zero-Inflated Poisson (ZIP) Models
by Paola Fersini, Michele Longo and Giuseppe Melisi
Risks 2025, 13(11), 214; https://doi.org/10.3390/risks13110214 - 3 Nov 2025
Viewed by 1361
Abstract
Usage-Based Insurance (UBI), also referred to as telematics-based insurance, has been experiencing a growing global diffusion. In addition to being well established in countries such as Italy, the United States, and the United Kingdom, UBI adoption is also accelerating in emerging markets such [...] Read more.
Usage-Based Insurance (UBI), also referred to as telematics-based insurance, has been experiencing a growing global diffusion. In addition to being well established in countries such as Italy, the United States, and the United Kingdom, UBI adoption is also accelerating in emerging markets such as Japan, South Africa, and Brazil. In Japan, telematics insurance has shown significant growth in recent years, with a steadily increasing subscription rate. In South Africa, UBI adoption ranks among the highest worldwide, with market penetration placing the country among the top three globally, just after the United States and Italy. In Brazil, UBI adoption is expanding, supported by government initiatives promoting road safety and innovation in the insurance sector. According to a MarketsandMarkets report of February 2025, the global UBI market is expected to grow from USD 43.38 billion in 2023 to USD 70.46 billion by 2030, with a compound annual growth rate (CAGR) of 7.2% over the forecast period. This growth is driven by the increasing adoption of both electric and internal combustion vehicles equipped with integrated telematics systems, which enable insurers to collect data on driving behavior and to tailor insurance premiums accordingly. In this paper, we analyze a large dataset consisting of trips recorded over five years from 100,000 policyholders across the Italian territory through the installation of black-box devices. Using univariate and multivariate statistical analyses, as well as Generalized Linear Models (GLMs) with Zero-Inflated Poisson distribution, we examine claims frequency and assess the relevance of various synthetic indicators of driving behavior, with the aim of identifying those that are most significant for insurance pricing. Full article
(This article belongs to the Special Issue Innovations in Non-Life Insurance Pricing and Reserving)
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