JMoE-FAP: A novel model for telecom network fraud victimization pattern analysis

Abstract

A comprehensive examination of the victimization process, coupled with the development of effective preventive strategies, represents the most promising approach for mitigating telecom network fraud. However, the limited availability of telecom fraud case text data hinders the advancement of robust data extraction algorithms, thereby complicating the identification of victimization patterns. To address this gap, this study proposes a victimization process analysis model that leverages mixed expert event joint extraction, utilizing real telecom fraud case data. The model integrates LERT-MoE to extract trigger words and arguments related to the victimization process from law enforcement reports, followed by the application of a dot-product attention mechanism for argument role classification. To the best of our knowledge, this represents the first attempt to apply a mixture-of-experts model with a purpose-built dot-product attention mechanism for the in-depth analysis of telecom network fraud victimization patterns, overcoming the limitations of previous methods in managing the complexity and diversity of fraudulent behaviors. Additionally, the Apriori method is employed to uncover prevalent behavioral patterns in the victimization process. Experimental results demonstrate that the proposed model outperforms baseline models in precision, accuracy, and F1-score for event extraction tasks in telecom fraud instances. Furthermore, the model identifies more granular fraud patterns within the victimization process, offering a valuable knowledge base for the development of targeted preventive strategies. The identified patterns can be used to design focused awareness campaigns, enhance fraud detection algorithms, and improve law enforcement training, thereby significantly increasing the effectiveness of anti-fraud initiatives.