Adversarial Attack Detection Using Explainable AI and Generative Models in Real-Time Financial Fraud Monitoring Systems

Abstract

The rising sophistication of adversarial attacks poses significant threats to the integrity and reliability of real-time financial fraud monitoring systems. As machine learning (ML) and deep learning models become more integrated into financial security infrastructures, they are increasingly vulnerable to subtle perturbations that can mislead fraud detection mechanisms. This review explores the intersection of explainable artificial intelligence (XAI) and generative models—such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)—in fortifying financial systems against adversarial threats. The study categorizes existing adversarial attack vectors targeting transaction-level anomaly detection and evaluates the limitations of conventional defense techniques. Furthermore, it assesses the role of XAI methods, such as SHAP and LIME, in interpreting model decisions to uncover adversarial behavior in a transparent and auditable manner. Generative models are reviewed both as tools for generating adversarial examples and for enhancing model robustness through adversarial training and anomaly simulation. Real-time constraints are also examined, including latency, scalability, and system responsiveness. The paper concludes by identifying research gaps and proposing an integrated framework that combines interpretability, real-time detection, and generative defense strategies for resilient and accountable fraud monitoring in financial ecosystems.