Telecom Fraud Detection via Hawkes-Enhanced Sequence Model

Yan Jiang, Guannan Liu, Junjie Wu, Hao Lin

Abstract: Detecting frauds from a massive amount of user behavioral data is often regarded as finding a needle in a haystack. While tremendous efforts have been devoted to fraud detection from behavioral sequences, existing studies rarely consider behavioral targets and companions and their interactions simultaneously in a sequence model. In this paper, we suggest extracting source and target neighbor sequences from the temporal bipartite network of user behaviors, and disclose the interesting correlation mode and repetition mode hidden inside the two types of sequences as important clues for fraudsters distinguishment. We then propose a novel Hawkes-enhanced sequence model (HESM) by integrating the Hawkes process into LSTM for historical influence learning. A historical attention mechanism is also proposed to enhance the strength of the long-term historical influence in response to the repetition mode. Moreover, in order to collectively model both types of neighbor sequences for capturing the correlation mode, we propose a correlation gate to control the information flow in sequences. We conduct extensive experiments on real-world datasets and demonstrate that HESM outperforms competitive baseline methods consistently in telecom fraud detection. Particularly, the abilities of HESM in historical influence leaning and sequence correlation learning have been explored visually and intensively.