Machine learning applications in nonlife insurance

Grize, Yves-Laurent; Fischer, Wolfram; Lützelschwab, Christian

doi:10.1002/asmb.2543

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Machine learning applications in nonlife insurance
Authors:	Grize, Yves-Laurent Fischer, Wolfram Lützelschwab, Christian
et. al:	No
DOI:	10.1002/asmb.2543
Published in:	Applied Stochastic Models in Business and Industry
Volume(Issue):	36
Issue:	4
Page(s):	523
Pages to:	537
Issue Date:	2020
Publisher / Ed. Institution:	Wiley
ISSN:	1524-1904 1526-4025
Language:	English
Subjects:	Dynamic pricing; Statistics in business and industry; Machine learning; Nonlife insurance; Retention model
Subject (DDC):	004: Computer science 332.38: Insurances
Abstract:	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. 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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/20550
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Data Analysis and Process Design (IDP)
Appears in collections:	Publikationen School of Engineering

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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.