Abstract: With the rapid development of electronic payments,the fraud problem is increasing.Limited by rule and feature engineering,traditional fraud detection methods are difficult to capture complex transaction patterns.Conversely,graph-based me-thods often downplay the significance of feature engineering while highlighting the relational aspect of the data.In addition,few studies have examined the application of graph methods in the field of fraud detection for specific bank transaction data.To address this problem,this paper proposes an end-to-end telecom fraud detection method,a fraud detection method for banking tran-sactions based on graph neural networks.The proposed method designs a feature engineering for graph models and trains it using a fusion model.Specifically,oversampling and node weighting are used to address the unbalanced dataset.Next,a user transaction graph model is built utilizing an adaptive similarity edge and node degree weight fusion technique to construct a user transaction graph model and mine potential correlation information between transaction nodes.Furthermore,model fusion is employed to merge local and global variables to overcome the constraints of separate classifiers.Experimental results show that in Guangxi Yulin Bank transaction data,the proposed model for the detection of transaction fraud dataon the three indicators of F1 score,recall rate,and AUC is improved by 1.65%,1.36% and 4.2% compared to GraphSAGE,respectively.The model also achieves a reduction of approximately 80% in training time.In comparison to other mainstream detection algorithms,it exhibits higher detection accuracy.

Key words: Bank transaction fraud detection, Feature engineering, Unbalanced treatment, Similarity edge, Graph neural network