An improve fraud detection framework via dynamic representations and adaptive frequency response filter.

The evolution of telecommunication technologies not only enhances social interactions but also inadvertently fosters an environment for telecom fraud. Graph-like data generated from traceable telecommunication interactions offers a foundation for graph-based fraud detection. However, the complexity and dynamism of interaction networks present formidable challenges. Our data exploration revealed that fraudsters exhibit consistent distinguishability from normal users in specific dynamic behavioral traits. Leveraging previous research, we constructed a latent synergy network (LSN) through second-order relations. Analysis of LSN exposed that fraudsters, seeking to elude detection, adopt deceptive behaviors by establishing connections with numerous normal users, leading to the over-smoothing problem in traditional GNN models. Consequently, we introduce the Dynamic Pattern with Adaptive Filter Graph Learning framework for telecom fraud detection. With the sequential network, we capture users' dynamic behavioral features for LSN input. Additionally, in LSN learning, we designed a trainable filter to capture differences between feature channels during information aggregation, mitigating the over-smoothing problem. On the Sichuan Telecom, Sichuan-mini Telecom, and YelpChi datasets, using AUC, Recall, and F1-score as evaluation metrics, DPGFD outperforms GCN, GraphSAGE, FRAUDRE, BWGNN, and GAGA by an average of 5%.