Generative hybrid models for fraud detection in auto insurance with a comparative analysis of VAE, GAN, and diffusion approaches

Fraud claim detection in auto insurance remains a vital yet complex challenge, mainly due to imbalanced data sets, non-linear feature interactions, and the necessity for explicable predictions. While traditional Machine Learning (ML) approaches show promise, they frequently struggle from poor genera...

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Veröffentlicht in:	Discover Artificial Intelligence Jg. 5; H. 1; S. 313 - 23
Hauptverfasser:	Bekkaye, Chadia, Oukhouya, Hassan, Zari, Tarek, Guerbaz, Raby, El Bouanani, Hicham
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V Springer
Schlagworte:	Accuracy Algorithms Artificial Intelligence Auto insurance Automobile insurance Classification Comparative analysis Computer Science Datasets Engineering Feature selection Fraud detection Fraud prevention Generative hybrid models Health insurance Insurance claims Insurance fraud Isolation forest Machine leaning algorithms Medical research Methods Neural networks Oversampling methods Support vector machines Survival analysis Classification metrics Isolation forest Performance comparison Oversampling methods Probabilistic calibration Auto insurance Machine leaning algorithms Fraud detection Model stability Generative hybrid models
ISSN:	2731-0809, 2731-0809
Online-Zugang:	Volltext
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