An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network

TL;DR

This paper presents a GAN-based adversarial attack method that can generate fraudulent insurance claims capable of bypassing detection systems with high success, highlighting vulnerabilities in current fraud detection models.

Contribution

It introduces a novel GAN-based attack approach for insurance fraud detection systems, demonstrating high success rates without requiring internal model knowledge.

Findings

Achieves 99% attack success rate on fraud detection systems

Shows that subtle modifications can bypass current models

Highlights need for robust defenses against adversarial attacks

Abstract

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.