A GAN-based adversarial attack is claimed to achieve 99% success in making fraudulent insurance claims look legitimate to LSTM and XGBoost fraud detectors, but the evidence is undermined by missing dataset and implementation details.
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An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
A GAN-based adversarial attack is claimed to achieve 99% success in making fraudulent insurance claims look legitimate to LSTM and XGBoost fraud detectors, but the evidence is undermined by missing dataset and implementation details.