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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorGrize, Yves-Laurent-
dc.contributor.authorFischer, Wolfram-
dc.contributor.authorLützelschwab, Christian-
dc.date.accessioned2020-10-01T14:53:06Z-
dc.date.available2020-10-01T14:53:06Z-
dc.date.issued2020-
dc.identifier.issn1524-1904de_CH
dc.identifier.issn1526-4025de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20550-
dc.description.abstractThe 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. 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. However, references for additional learning materials are provided.de_CH
dc.language.isoende_CH
dc.publisherWileyde_CH
dc.relation.ispartofApplied Stochastic Models in Business and Industryde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectDynamic pricingde_CH
dc.subjectStatistics in business and industryde_CH
dc.subjectMachine learningde_CH
dc.subjectNonlife insurancede_CH
dc.subjectRetention modelde_CH
dc.subject.ddc004: Informatikde_CH
dc.subject.ddc332.38: Versicherungende_CH
dc.titleMachine learning applications in nonlife insurancede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1002/asmb.2543de_CH
zhaw.funding.euNode_CH
zhaw.issue4de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end537de_CH
zhaw.pages.start523de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume36de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedFinTechde_CH
zhaw.webfeedIndustrie 4.0de_CH
zhaw.webfeedPredictive Analyticsde_CH
zhaw.webfeedStatistik und Quantitative Financede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Grize, Y.-L., Fischer, W., & Lützelschwab, C. (2020). Machine learning applications in nonlife insurance. Applied Stochastic Models in Business and Industry, 36(4), 523–537. https://doi.org/10.1002/asmb.2543
Grize, Y.-L., Fischer, W. and Lützelschwab, C. (2020) ‘Machine learning applications in nonlife insurance’, Applied Stochastic Models in Business and Industry, 36(4), pp. 523–537. Available at: https://doi.org/10.1002/asmb.2543.
Y.-L. Grize, W. Fischer, and C. Lützelschwab, “Machine learning applications in nonlife insurance,” Applied Stochastic Models in Business and Industry, vol. 36, no. 4, pp. 523–537, 2020, doi: 10.1002/asmb.2543.
GRIZE, Yves-Laurent, Wolfram FISCHER und Christian LÜTZELSCHWAB, 2020. Machine learning applications in nonlife insurance. Applied Stochastic Models in Business and Industry. 2020. Bd. 36, Nr. 4, S. 523–537. DOI 10.1002/asmb.2543
Grize, Yves-Laurent, Wolfram Fischer, and Christian Lützelschwab. 2020. “Machine Learning Applications in Nonlife Insurance.” Applied Stochastic Models in Business and Industry 36 (4): 523–37. https://doi.org/10.1002/asmb.2543.
Grize, Yves-Laurent, et al. “Machine Learning Applications in Nonlife Insurance.” Applied Stochastic Models in Business and Industry, vol. 36, no. 4, 2020, pp. 523–37, https://doi.org/10.1002/asmb.2543.


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