The rise of Bitcoin has revolutionized the financial landscape, but it has also opened the door to a new era of criminal activities. Criminals take advantage of the anonymity provided by Bitcoin to conduct illicit transactions and engage in fraudulent activities. To address this issue, this paper proposes a detection model using Graph Neural Networks (GNNs) to detect fraudulent activities in the complex financial systems of Bitcoin. From the GNNs, we use EvolveGCN and EvolveGGCN to compare between them and find a powerful model that can investigate the network construction of financial transactions and capture patterns and anomalies that traditional methods may miss. In the literature, there have been a limited number of studies on Bitcoin fraud detection using GNNs, especially EvolveGGCN. Therefore, in this paper, we focus on the detection of fraud in the Bitcoin network using EvolveGCN and EvolveGGCN. In addition, we used a more recent dataset called Elliptic++, which is an extension of the Elliptic Dataset. The dataset provides valuable information on the behavior and patterns of fraudulent actions in the Bitcoin network. The results show that EvolveGGCN outperforms other models in terms of precision, recall, F1 score, and micro-F1 score. With an F1-score of 0.90 and micro-F1 of 0.93 for detecting illicit transactions in the early time steps.