Improving graph-based label propagation algorithm with group partition for fraud detection

摘要

Fraudulent user detection is a crucial issue in financial risk management. Due to the lack of labeled data and the reliability of labeling, label propagation algorithms (LPA) are effective solutions in this scenario. Most existing models only propagate the risk probabilities for individual users through feature level, while ignoring the real-world graph structure and the characteristics of gang crime. This paper improves the graph-based LPA through group partition, which can be directly implemented on the graph at hand with full consideration of the group information. The exhaustive experimental results testify the performance of our proposed model KGLPA over other off-the-shelf models and amend the insufficiency of feature-based LPA with higher reliability and stability to improve the detection of fraudulent users and secure the marketing budgets.