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
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
Other Objects (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
Detecting review fraud using metaheuristic graph neural networks
The impact of insurance fraud detection systems
Healthcare insurance fraud detection using data mining
Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning
Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring
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
Detecting review fraud using metaheuristic graph neural networks
The impact of insurance fraud detection systems
Healthcare insurance fraud detection using data mining
Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning
Detection of Fraud in a Clinical Trial Using Unsupervised Statistical Monitoring
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
Detecting review fraud using metaheuristic graph neural networks
The impact of insurance fraud detection systems
Healthcare insurance fraud detection using data mining
Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning