Benedek, Botond and Nagy, Bálint Zsolt (2023) Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance. FINANCIAL AND ECONOMIC REVIEW, 22 (2). pp. 77-98. ISSN 2415–9271 (print), 2415–928X (online)
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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. Journal of Economic Literature (JEL) codes: G22, C14, C45
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | automobile insurance, insurance fraud, fraud detection, cost-sensitive decision-making, data mining |
| Subjects: | H Social Sciences / társadalomtudományok > HB Economic Theory / közgazdaságtudomány H Social Sciences / társadalomtudományok > HG Finance / pénzügy |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 30 Jun 2023 06:36 |
| Last Modified: | 30 Jun 2023 06:36 |
| URI: | http://real.mtak.hu/id/eprint/168798 |
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