Telecom fraud causes harm to users, telecom operators, and even the stability of society. Detecting telecom fraud users through automated algorithms can prevent the occurrence of telecom fraud incidents. However, the complexity, variability, and concealment of telecom fraud behaviors pose significant challenges to detection algorithms. In this paper, in response to these challenges, we propose a new multivariate time series classification model, Bidirectional Information Fusion Time Series Transformer (BIFTST), for achieving high-performance telecom fraud detection. We utilize multiscale patches and the transformer to capture multiscale features, employ a segmentation module to divide the time series into segments, use a Bidirectional cross attention fusion module to effectively learn dynamic and static data, and integrate multiscale features using a gating fusion network. Additionally, we have collected a telecom fraud dataset that includes users’ multivariate time series data and static basic information. We have trained the model and conducted related experiments on this dataset. The experimental results show that our method achieves excellent results on the telecom fraud dataset and the effectiveness of each module of the model is verified through ablation experiments.