New Findings in Machine Learning Described from University of Munich (Detecting Insurance Fraud Using Supervised and Unsupervised Machine Learning): Machine Learning

2023 JUN 20 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Policy and Law Daily -- Investigators discuss new findings in Machine Learning. According to news reporting out of Munich, Germany, by NewsRx editors, research stated, “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.”

Our news journalists obtained a quote from the research from the University of Munich, “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.”

According to the news editors, the research concluded: “Therefore, for implementation, we suggest understanding supervised and unsupervised methods as complements rather than substitutes.”

This research has been peer-reviewed.

For more information on this research see: Detecting Insurance Fraud Using Supervised and Unsupervised Machine Learning. Journal of Risk and Insurance, 2023. Journal of Risk and Insurance can be contacted at: Wiley, 111 River St, Hoboken 07030-5774, NJ, USA. (Wiley-Blackwell - http://www.wiley.com/; Journal of Risk and Insurance - http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1539-6975)

Our news journalists report that additional information may be obtained by contacting Johannes Kriebel, University of Munich, Finance Ctr Munster, Munich, Germany. Additional authors for this research include Joern Debener and Volker Heinke.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1111/jori.12427. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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