Abstract: In recent years,the digitization of mobile terminal equipment has risen sharply,and fraudulent behaviors in the credit industry have shown new characteristics such as dynamic development,concealment of behavior,and professional camouflage.The cross-order growth of massive data has brought considerable challenges to the effectiveness and computational efficiency of traditional anti-fraud algorithms.Therefore,this paper aims to fully learn the interaction information between different entities in the credit scene,reduce the computational consumption of the algorithm to make it suitable for large-scale graph data tasks,and propose a specific group mining algorithm BKH-II(Bron-Kerbosh-H-II) based on heterogeneous information networks.First,defining and classifing the credit entities and the relationships between them in the source data,and using the similarity between different entities as the relationship weight to build a credit heterogeneous information network.A two-stage H-graph-based maximal clique enumeration algorithm is adopted for the network to mine unique groups.Finally,potential fraud groups are obtained through local feature engineering correction and division.Experiments prove that the accuracy of BKH-II on the four evaluation indicators is NMI=0.983,NRI=0.96,F-score=0.943,Omega=0.95,and shows good generalization and low computational complexity.

Key words: Heterogeneous information network, Credit anti-fraud, Specific group mining, Community discovery, Graph embedding