Credit card transaction fraud detection based on DB-SVMSmote-ANN

In response to the issues of extremely unbalanced sample distribution, high-dimensional samples, and large volumes of data in the problem of credit card transaction fraud detection, this paper proposes a hybrid model that combines DBSCAN, SVMSmote, and Artificial Neural Networks (ANN), namely the DB-SVMSmote-ANN model. This model can generate samples of the minority class, addressing the issue of too few positive samples in credit card transaction fraud datasets. Subsequently, the balanced dataset is used to train the ANN classification model, solving the classification problem between fraudulent and normal transaction samples. In the experimental process of this paper, the differences between balanced and unbalanced datasets were first compared, followed by a comparison of the effects of using the DBSVMSmote-ANN and other classification models. Ultimately, the experiments demonstrated that the DB-SVMSmoteANN model can excellently solve the problem of credit card transaction fraud detection.