Behavior Language Processing with Graph based Feature Generation for Fraud Detection in Online Lending
نویسندگان
چکیده
Online lending has exploded in China in recent years. However, the nancial agents are vulnerable for fraud aacks which reults in huge nancial losses. Anti-fraud detection methods for traditional nancial services are less eective against online frauds. As a group eort at CreditX, we designed an accurate, ecient, and scalable online fraud detecting mechnism by delivering a behavior language processing (BLP) framework. Our solution integrates multiple layers from user online behavior data acquisition, knowledge graph building, feature extraction, to nal predictive models. As a core component of BLP, we applied graph phomophily theory on selecting social relationships to build a fraud centric bipartite graph. Key graph features are generated by combining graph thoery and experts’ domain knowledge to capture linked fraudulous behaviors. e results of online fraud detection on massive realworld data have shown our graph based feature extraction method signicantly boosts the accuracy and eectiveness of BLP model.
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