Adversarially Robust and Explainable AI for Real-Time Financial Fraud Detection in High-Frequency Transactions

The rapid expansion of digital financial transactions has led to an increase in sophisticated fraud techniques, particularly within high-frequency trading and online banking systems. Many existing fraud detection models struggle to detect adversarially crafted fraudulent transactions, where malicious actors exploit system vulnerabilities to evade detection. Additionally, traditional artificial intelligence (AI)-based fraud detection mechanisms often lack transparency, making it difficult for financial analysts and regulatory bodies to understand how decisions are made. This lack of interpretability raises concerns regarding compliance with financial regulations. To address these challenges, this paper introduces a novel fraud detection framework that integrates adversarial training with explainable AI (XAI) techniques for real-time fraud detection in high-frequency transactions. Our approach enhances model resilience against adversarial attacks by utilizing generative adversarial networks (GANs) and adversarial retraining, allowing the model to recognize and counteract fraudulent attempts more effectively. Furthermore, the incorporation of SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) provides interpretable decision-making, ensuring transparency and regulatory compliance. The proposed method is validated using real-world financial transaction datasets, benchmarking its performance against conventional machine learning models such as random forests, gradient boosting machines, and recurrent neural networks (RNNs). Experimental results demonstrate that our model significantly enhances fraud detection rates while maintaining high precision and recall, effectively reducing false negatives. Additionally, the XAI components facilitate clear model interpretability, enabling financial institutions to trust and adopt AI-driven fraud detection systems. This research contributes to the advancement of secure and interpretable AI applications in financial technology, paving the way for robust fraud prevention strategies in high-risk financial environments.