Journal: European Actuarial Journal

Abbreviation

Eur. Actuar. J.

Publisher

Springer

Journal Volumes

ISSN

2190-9733
2190-9741

Description

Filters

2011 - 2019132020 - 202510
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Publications 1 - 10 of 23
  • Asset-liability management for long-term insurance business
    Item type: Journal Article
    Albrecher, Hansjörg; Bauer, Daniel; Embrechts, Paul; et al. (2018)
    European Actuarial Journal
  • Insurance: models, digitalization, and data science
    Item type: Journal Article
    Albrecher, Hansjörg; Bommier, Antoine; Filipović, Damir; et al. (2019)
    European Actuarial Journal
  • Making Tweedie’s compound Poisson model more accessible
    Item type: Journal Article
    Delong, Łukasz; Lindholm, Mathias; Wüthrich, Mario V. (2021)
    European Actuarial Journal
    The most commonly used regression model in general insurance pricing is the compound Poisson model with gamma claim sizes. There are two different parametrizations for this model: the Poisson-gamma parametrization and Tweedie’s compound Poisson parametrization. Insurance industry typically prefers the Poisson-gamma parametrization. We review both parametrizations, provide new results that help to lower computational costs for Tweedie’s compound Poisson parameter estimation within generalized linear models, and we provide evidence supporting the industry preference for the Poisson-gamma parametrization.
  • Parameter reduction in log-normal chain-ladder models
    Item type: Journal Article
    Wüthrich, Mario V.; Verrall, Richard J. (2015)
    European Actuarial Journal
  • Machine learning techniques for mortality modeling
    Item type: Journal Article
    Deprez, Philippe; Shevchenko, Pavel V.; Wüthrich, Mario V. (2017)
    European Actuarial Journal
  • Modeling accounting year dependence in runoff triangles
    Item type: Journal Article
    Salzmann, Robert; Wüthrich, Mario V. (2012)
    European Actuarial Journal
  • Covariate selection from telematics car driving data
    Item type: Journal Article
    Wüthrich, Mario V. (2017)
    European Actuarial Journal
    Car insurance companies have started to collect high-frequency GPS location data of their car drivers. This data provides detailed information about the driving habits and driving styles of individual car drivers. We illustrate how this data can be analyzed using techniques from pattern recognition and machine learning. In particular, we describe how driving styles can be categorized so that they can be used for a regression analysis in car insurance pricing.
  • Model selection with Gini indices under auto-calibration
    Item type: Journal Article
    Wüthrich, Mario V. (2023)
    European Actuarial Journal
    The Gini index does not give a strictly consistent scoring function. Therefore, simply maximizing the Gini index may lead to a wrong model choice. The main issue is that the Gini index is a rank-based score that is not calibration-sensitive. We show that the Gini index allows for strictly consistent scoring if we restrict it to the class of auto-calibrated regression models. That is, on the class of auto-calibrated models we know that the true model maximizes the Gini index.
  • Neural networks applied to chain–ladder reserving
    Item type: Journal Article
    Wüthrich, Mario V. (2018)
    European Actuarial Journal
    Classical claims reserving methods act on so-called claims reserving triangles which are aggregated insurance portfolios. A crucial assumption in classical claims reserving is that these aggregated portfolios are sufficiently homogeneous so that a coarse reserving algorithm can be applied. We start from such a coarse reserving method, which in our case is Mack’s chain–ladder method, and show how this approach can be refined for heterogeneity and individual claims feature information using neural networks.
  • Eliciting claims development patterns and costs hidden in backlogs
    Item type: Journal Article
    Lindskog, Filip; Wüthrich, Mario V. (2025)
    European Actuarial Journal
    Random delays between the occurrence of accident events and the corresponding reporting times of insurance claims is a standard feature of insurance data. The time lag between the reporting and the processing of a claim depends on whether the claim can be processed without delay as it arrives or whether it remains unprocessed for some time because of temporarily insufficient processing capacity that is shared between all incoming claims. We aim to explain and analyze the nature of processing delays and build-up of backlogs. Development patterns for incoming reported claims that form the basis for claims reserving may be distorted by backlogs when transformed into processed (or paid) claims. In a first step, we show how to infer hidden development patterns from processed claims data. In a second step, we discuss how backlogs impact claims costs, and we show how to select processing capacity optimally in order to minimize claims costs, taking delay-adjusted costs and fixed costs for claims settlement capacity into account. Theoretical results are combined with a large-scale numerical study that demonstrates practical usefulness of our proposal.
Publications 1 - 10 of 23