Fraudsters could bypass AI-powered insurance checks using adversarial GANs

A newly published study reveals how Generative Adversarial Networks (GANs) can be exploited to systematically bypass state-of-the-art insurance fraud detection systems. The paper, titled “An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network” and published on arXiv, introduces an adversarial attack framework capable of tricking fraud detection models into classifying fraudulent claims as legitimate, with an alarming 99% attack success rate.

This research signals a profound security vulnerability in existing artificial intelligence (AI)-based systems that insurance providers increasingly rely upon to safeguard against fraudulent activity. With only limited access to model outputs and without the original training data, attackers can manipulate insurance records and potentially cause systemic failures in fraud monitoring infrastructure.

How can GANs be weaponized against fraud detection models?

At the core of the attack is a GAN - a deep learning architecture composed of two competing neural networks: a generator and a discriminator. The generator crafts synthetic samples designed to appear legitimate, while the discriminator attempts to differentiate between genuine and fake claims. As both networks train together, the generator improves at mimicking real data so convincingly that it can bypass detection.

The study extends this idea by incorporating reinforcement learning and surrogate models, simulating realistic black-box attack scenarios where internal model details are unavailable. Even under these constraints, the generated fraudulent inputs succeeded in deceiving detection systems with near-perfect reliability. This was achieved through a carefully designed optimization loop using binary cross-entropy loss and the Adam optimizer to guide the GAN in producing increasingly effective adversarial examples.

What makes this development particularly concerning is its practicality. The researchers designed the system to operate in both white-box (full access to model and data) and gray-box (access only to model outputs) conditions. Even in the latter, their method performed with a 99% attack success rate across different classifiers, including LSTM and XGBoost models. This indicates that real-world insurance fraud systems, often opaque and proprietary, could still be compromised using such techniques.

How effective are current models at detecting insurance fraud?

To evaluate baseline robustness, the researchers tested five widely-used machine learning models: LSTM, XGBoost, LightBoost, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). Among these, XGBoost emerged as the top performer under standard, non-adversarial conditions, achieving an accuracy of 82.5% and an F1 score of 0.819. LightBoost followed closely with similar metrics. The LSTM model, though widely used for sequence modeling, struggled in this context, scoring just 0.419 in F1 despite a reasonable 75% accuracy rate, suggesting high false positives or negatives.

To enhance interpretability, the authors applied SHAP (Shapley Additive Explanations) to both LSTM and XGBoost models, determining which features most heavily influenced predictions. Incident severity emerged as the most influential input variable across models. However, divergences appeared in secondary features: while LSTM emphasized insured hobbies, ZIP codes, and vehicle claims, XGBoost prioritized vehicle claims, umbrella limits, and hobbies. These differences in feature weighting suggest that model-specific biases could impact fraud detection outcomes and resilience to manipulation.

The experiment used a publicly available insurance dataset comprising 1,000 records and 38 features. The data was normalized and divided into training, validation, and test sets to ensure methodological robustness. Through this controlled environment, researchers could systematically test the models under different adversarial attack techniques, including FGSM, BIM, PGD, and Random Noise.

Can current systems survive adversarial attacks?

The research highlights a grave vulnerability: most models, regardless of accuracy under normal conditions, showed significant performance degradation under adversarial stress. Attacks using FGSM, BIM, and PGD all substantially reduced model accuracy, especially as the attack intensity (denoted by epsilon values) increased.

For instance, XGBoost, typically robust under standard conditions, saw its accuracy plummet under random noise and white-box attacks. When attacked using the proposed GAN-based method, both LSTM and XGBoost experienced a catastrophic drop in accuracy to just 1%, confirming the effectiveness of the adversarial approach. Notably, this GAN method required no access to internal model architecture or training data, making it highly feasible in real-world cyberattack scenarios.

Traditional models like KNN and SVM were not immune either. Their non-differentiable structures made them incompatible with gradient-based attacks like FGSM or PGD, but they remained susceptible to the GAN-based adversarial strategy. The research team circumvented gradient access challenges by employing reinforcement learning to adaptively steer the generator towards creating undetectable fraudulent claims.

Comparative performance metrics from the study’s experiments indicate that while classical adversarial attacks degraded model reliability, the GAN-based approach outperformed all baselines in deception efficacy. For a system trained without adversarial robustness, no reliable defense currently exists against such a stealthy and sophisticated intrusion method.

What does this mean for the future of fraud detection?

As the insurance industry continues to integrate AI into operational workflows, adversaries may weaponize these vulnerabilities to exploit systemic gaps. The fact that even high-performing models like XGBoost can be fooled without model access reveals a security blind spot in current deployment strategies.

The authors call for urgent investments in countermeasures, such as adversarial training, anomaly detection systems, and uncertainty-aware AI models. Without such safeguards, the financial and reputational damage from exploited fraud detection systems could be immense. Moreover, the study underscores the importance of explainability and feature transparency, so that detection pipelines can be monitored for signs of adversarial manipulation.