risk-scoring

This paper proposes a graph neural network framework for financial fraud detection that integrates transaction records and identity information into node attributes, employs a multi-layer message passing mechanism, and uses weighted supervision and structural consistency regularization to improve risk scoring and probability calibration. Experiments on a public dataset show the method outperforms existing approaches.