Adaptive financial fraud detection using graph neural networks and reinforcement learning

Fraud detection in financial systems is critical to maintain security and trust in these systems. Traditional fraud detection methods often struggle with the dynamic fraud patterns, and this requires method that can adapt in real-time. Traditional systems require large volumes of labeled data which is difficult to obtain given the private nature of financial systems. In this work, we introduce a framework that utilizes reinforcement learning to enhance fraud detection capabilities.  We treat fraud detection as a sequential decision-making process. Reinforcement learning agents can learn and refine optimal strategies to identify fraudulent activities as they adapt to new transaction data. The method combines transaction history, and user behavior to enhance detection efficiency. Through evaluation on benchmark datasets, we show that this approach significantly improves detection rates and reduces the incidence of false positives which can hurt business profits. It makes the systems robust while facilitating real-time processing. Our results suggest that reinforcement learning agents, when combined with well-designed reward mechanisms, can outperform traditional models in detecting fraudulent activities.