Anomaly detection in financial transactions poses significant privacy challenges. This paper introduces a federated learning (FL) framework for Privacy-Preserving Behavioral Anomaly Detection using Graph Neural Networks (GNNs) on dynamic graphs to model cardholder transactions. We incorporate anonymization-based and noise-based privacy-preserving methods for feature engineering and a domain-specific negative sampling technique to train models without labeled data, making it suitable for real-world applications. Our results, benchmarked on synthetic and real-world datasets, show that deep learning-based outperform clustering-based methods, with F1-scores of 0.91 ± 0.02 and 0.87 ± 0.04, respectively. Additionally, using the anomaly score as a feature in fraud detection models yields a 1.76% ± 0.54% improvement in F1-score, enhancing fraud detection performance while preserving privacy.