Artikel

Detecting insurance fraud using supervised and unsupervised machine learning

Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied in this context, and there remains insufficient evidence to guide the choice between these branches of machine learning for insurance fraud detection. Accordingly, this study evaluates supervised and unsupervised learning using proprietary insurance claim data. Furthermore, we conduct a field experiment in cooperation with an insurance company to investigate the performance of each approach in terms of identifying new fraudulent claims. We derive several important findings. Unsupervised learning, especially isolation forests, can successfully detect insurance fraud. Supervised learning also performs strongly, despite few labeled fraud cases. Interestingly, unsupervised and supervised learning detect new fraudulent claims based on different input information. Therefore, for implementation, we suggest understanding supervised and unsupervised methods as complements rather than substitutes.

Language
Englisch

Bibliographic citation
Journal: Journal of Risk and Insurance ; ISSN: 1539-6975 ; Volume: 90 ; Year: 2023 ; Issue: 3 ; Pages: 743-768 ; Hoboken, NJ: Wiley

Classification
Wirtschaft
Subject
insurance fraud detection
machine learning
supervised learning
unsupervised learning

Event
Geistige Schöpfung
(who)
Debener, Jörn
Heinke, Volker
Kriebel, Johannes
Event
Veröffentlichung
(who)
Wiley
(where)
Hoboken, NJ
(when)
2023

DOI
doi:10.1111/jori.12427
Last update
10.03.2025, 11:42 AM CET

Data provider

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Artikel

Associated

  • Debener, Jörn
  • Heinke, Volker
  • Kriebel, Johannes
  • Wiley

Time of origin

  • 2023

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