Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization

ArXi:2605.12782v1 Announce Type: new Financial transaction fraud prevention faces challenges such as complex relationship structures, concealed behavioral patterns, and dynamically changing data distribution. Discrimination models relying solely on independent sample features are insufficient to fully characterize the risks of group collaboration and chain transfers within transaction networks. This paper proposes a graph neural network representation learning and risk discrimination framework for financial transaction fraud prevention.