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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Journal of Risk and Insurance ; ISSN: 1539-6975 ; Volume: 90 ; Year: 2023 ; Issue: 3 ; Pages: 743-768 ; Hoboken, NJ: Wiley
- Klassifikation
-
Wirtschaft
- Thema
-
insurance fraud detection
machine learning
supervised learning
unsupervised learning
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Debener, Jörn
Heinke, Volker
Kriebel, Johannes
- Ereignis
-
Veröffentlichung
- (wer)
-
Wiley
- (wo)
-
Hoboken, NJ
- (wann)
-
2023
- DOI
-
doi:10.1111/jori.12427
- Letzte Aktualisierung
-
20.09.2024, 08:25 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Debener, Jörn
- Heinke, Volker
- Kriebel, Johannes
- Wiley
Entstanden
- 2023
Ähnliche Objekte (12)
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Applications of supervised and unsupervised ensemble methods
Detecting derogatory compounds – an unsupervised approach
Detecting derogatory compounds – an unsupervised approach
Active data selection in supervised and unsupervised learning
Supervised and unsupervised ensemble methods and their applications
Riemannian Flows for Supervised and Unsupervised Geometric Image Labeling
Supervised and unsupervised ensemble learning for the semantic web
Supervised and unsupervised analyses in next generation sequencing data
Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
Supervised and unsupervised methods for learning representations of linguistic units
Graph-based methods for unsupervised and semi-supervised data analysis
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Applications of supervised and unsupervised ensemble methods
Detecting derogatory compounds – an unsupervised approach
Detecting derogatory compounds – an unsupervised approach
Active data selection in supervised and unsupervised learning
Supervised and unsupervised ensemble methods and their applications
Riemannian Flows for Supervised and Unsupervised Geometric Image Labeling
Supervised and unsupervised ensemble learning for the semantic web
Supervised and unsupervised analyses in next generation sequencing data
Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
Supervised and unsupervised methods for learning representations of linguistic units
Graph-based methods for unsupervised and semi-supervised data analysis
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Applications of supervised and unsupervised ensemble methods
Detecting derogatory compounds – an unsupervised approach
Detecting derogatory compounds – an unsupervised approach
Active data selection in supervised and unsupervised learning
Supervised and unsupervised ensemble methods and their applications
Riemannian Flows for Supervised and Unsupervised Geometric Image Labeling
Supervised and unsupervised ensemble learning for the semantic web
Supervised and unsupervised analyses in next generation sequencing data
Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
Supervised and unsupervised methods for learning representations of linguistic units