Template-Type: ReDIF-Article 1.0

Author-Name: Botond Benedek
Author-Workplace-Name: Babes-Bolyai University, Cluj-Napoca
Author-Email: botond.benedek@econ.ubbcluj.ro
Author-Person: 

Author-Name: Balint Zsolt Nagy
Author-Workplace-Name: Babes-Bolyai University, Cluj-Napoca
Author-Email: balint.nagy@econ.ubbcluj.ro
Author-Person: 

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

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.

Classification-JEL: G22, C14, C45
Keywords: automobile insurance, insurance fraud, fraud detection, cost-sensitive decision-making, data mining
Pages: 77-98
Volume: 22
Issue: 2
Year: 2023
File-URL: https://en-hitelintezetiszemle.mnb.hu/letoltes/fer-22-2-st3-benedek-nagy.pdf
File-Format: Application/pdf
Handle: RePEc:mnb:finrev:v:22:y:2023:i:2:p:77-98