Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance

Botond Benedek () and Balint Zsolt Nagy ()
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Botond Benedek: Babes-Bolyai University, Cluj-Napoca
Balint Zsolt Nagy: Babes-Bolyai University, Cluj-Napoca

Financial and Economic Review, 2023, vol. 22, issue 2, 77-98

Abstract: Business practice and various industry reports all show that automobile insurance fraud is very common, which is why effective fraud detection is so important. In our study, we investigate whether today's widespread AI-based fraud detection methods are more effective from a financial (cost-effectiveness) point of view than methods based on traditional statistical-econometric tools. Based on our results, we came to the unexpected conclusion that the current AI-based automobile insurance fraud detection methods tested on a real database found in the literature are less cost-effective than traditional statistical-econometric methods.

Keywords: automobile insurance; insurance fraud; fraud detection; cost-sensitive decision-making; data mining (search for similar items in EconPapers)
JEL-codes: C14 C45 G22 (search for similar items in EconPapers)
Date: 2023
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