Studies from Zurich University of Applied Sciences Have Provided New Data on Machine Learning (Machine Learning Applications In Nonlife Insurance)

2021 FEB 01 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Current study results on Machine Learning have been published. According to news reporting from Winterthur, Switzerland, by NewsRx journalists, research stated, “The literature on analytical applications in insurance tends to be either very general or rather technical, which may hold back the adoption of new important tools by industrial practitioners. Our goal is to stress that machine learning (ML) algorithms will play a significant role in the insurance industry in the near future and thus to encourage practitioners to learn and apply these techniques.”

The news correspondents obtained a quote from the research from the Zurich University of Applied Sciences, “After discussing the increasing relevance of data for nonlife insurance and briefly reviewing the major impact of digital technology on this business, we restrict our discussion to technical analytical applications and indicate where ML algorithms can add most value. We present two real examples: first a comparison of retention models for household insurance and then a dynamic pricing problem for online motor insurance. Both applications illustrate the advantages but also some of the difficulties of applying ML tools in practice. Finally, we mention some challenges posed by the use of ML in the industry and formulate a few recommendations for successful applications in insurance. This article is neither a tutorial nor an exhaustive review of technical ML applications in nonlife insurance.”

According to the news reporters, the research concluded: “However, references for additional learning materials are provided.”

This research has been peer-reviewed.

For more information on this research see: Machine Learning Applications In Nonlife Insurance. Applied Stochastic Models in Business and Industry, 2020;36(4):523-537. Applied Stochastic Models in Business and Industry can be contacted at: Wiley (John Wiley & Sons), 111 River St, Hoboken 07030-5774, NJ, USA. (Wiley-Blackwell - http://www.wiley.com/; Applied Stochastic Models in Business and Industry - http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1526-4025)

Our news journalists report that additional information may be obtained by contacting Yves-Laurent Grize, Zurich University of Applied Sciences, School of Engineering, Ch-8401 Winterthur, Switzerland. Additional authors for this research include Wolfram Fischer and Christian Lutzelschwab.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1002/asmb.2543. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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