Research and Application of Bank Anti-fraud Model Based on Knowledge Graph
In response to the issue that traditional bank anti-fraud models no longer meet the timeliness and accura-cy requirements for fraud detection,this article proposes a knowledge graph-based anti-fraud model.This model uses big data based on multi-source information and high-dimensional derived features to build a knowledge graph,conduct a comprehensive profiling of credit individuals,analyze the associated relationships,and extract network attributes.Then,risk features are mined from four aspects and two dimensions.The four aspects refer to personal basic information,account information,credit history,and behavioral information,while the two dimensions refer to personal nodes and network structure.Finally,the risk features are substituted into LightGBM to determine whether it is a fraudulent type and obtain the corresponding probability.Experiments show that,compared to mod-els using only personal characteristics,models using both personal and network characteristics perform better,with AUC and F1 scores increasing by 5.18%and 5.71%respectively.Therefore,this solution can effectively provide fraud assessment for banks'personal credit.
bank anti-fraudfeature derivationknowledge graphLightGBM
NETL
NSTL
万方数据
