This article proposes a financial anti-fraud model based on a Dual-Channel Graph Attention Network (DGAN) integrated with blockchain technology. The model combines a Node Attention Network and a Semantic Attention Network to construct a Dual-Head Attention Network module, enabling comprehensive analysis of complex relationships in user transaction data. It also integrates a Gradient-Boosting Decision Tree (GBDT) to enhance fraud detection, resulting in the GBDT-DGAN model. Blockchain is used to ensure data privacy and secure transmission. Experimental results show the model achieves an accuracy of 93.82%, a 5.76% improvement over baseline algorithms like CNN. The recall and F1 scores are 89.5% and 81.66%, respectively, with a packet loss rate below 7%. The model demonstrates superior performance in fraud detection and network data security. The integration of GBDT-DGAN with blockchain provides an efficient and secure solution for financial fraud detection. The study highlights the effectiveness of the proposed model in improving fraud detection accuracy and ensuring data privacy. However, limitations such as data quality, model generalization, and potential biases are noted, suggesting the need for further research and optimization. The model offers a promising approach for enhancing financial security and fraud prevention.This article proposes a financial anti-fraud model based on a Dual-Channel Graph Attention Network (DGAN) integrated with blockchain technology. The model combines a Node Attention Network and a Semantic Attention Network to construct a Dual-Head Attention Network module, enabling comprehensive analysis of complex relationships in user transaction data. It also integrates a Gradient-Boosting Decision Tree (GBDT) to enhance fraud detection, resulting in the GBDT-DGAN model. Blockchain is used to ensure data privacy and secure transmission. Experimental results show the model achieves an accuracy of 93.82%, a 5.76% improvement over baseline algorithms like CNN. The recall and F1 scores are 89.5% and 81.66%, respectively, with a packet loss rate below 7%. The model demonstrates superior performance in fraud detection and network data security. The integration of GBDT-DGAN with blockchain provides an efficient and secure solution for financial fraud detection. The study highlights the effectiveness of the proposed model in improving fraud detection accuracy and ensuring data privacy. However, limitations such as data quality, model generalization, and potential biases are noted, suggesting the need for further research and optimization. The model offers a promising approach for enhancing financial security and fraud prevention.