用生成对抗网络伪造医保骗保单，99%成功率绕过检测
An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
- 基于GAN生成虚假理赔数据，无需了解模型细节
- 仅修改真实记录就能让骗保单骗过检测系统，成功率99%
- 揭示现有系统脆弱性，适合安全研究者关注
医保欺诈检测是现代保险服务的关键进展，提供智能化与数字化监控以提升管理效率并防范欺诈。尽管人工智能与机器学习在检测欺诈案件方面表现优异，但缺乏标准化防御机制使得当前系统易受新型对抗性攻击威胁。本文提出一种基于生成对抗网络（GAN）的攻击方法，可对欺诈检测系统实施对抗攻击。结果显示，攻击者在不了解训练数据或内部模型细节的情况下，仍能生成被分类为合法的欺诈案例，攻击成功率达99%。通过微调真实保险记录与理赔信息，攻击者可显著提高欺诈风险，从而绕过存在缺陷的检测系统。该研究凸显了提升医保欺诈检测模型对抗抗扰动能力的紧迫性，有助于保障各类保险系统的稳定性与可靠性。
原文摘要 · Abstract (English)
Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security and efficiency of insurance systems. Although AI and machine learning algorithms have demonstrated strong performance in detecting fraudulent claims, the absence of standardized defense mechanisms renders current systems vulnerable to emerging adversarial threats. In this paper, we propose a GAN-based approach to conduct adversarial attacks on fraud detection systems. Our results indicate that an attacker, without knowledge of the training data or internal model details, can generate fraudulent cases that are classified as legitimate with a 99\% attack success rate (ASR). By subtly modifying real insurance records and claims, adversaries can significantly increase the fraud risk, potentially bypassing compromised detection systems. These findings underscore the urgent need to enhance the robustness of insurance fraud detection models against adversarial manipulation, thereby ensuring the stability and reliability of different insurance systems.
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