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Identity fraud detection is of great importance in many real-world scenariossuch as the financial industry. However, few studies addressed this problembefore. In this paper, we focus on identity fraud detection in loanapplications and propose to solve this problem with a novel interactivedialogue system which consists of two modules. One is the knowledge graph (KG)constructor organizing the personal information for each loan applicant. Theother is structured dialogue management that can dynamically generate a seriesof questions based on the personal KG to ask the applicants and determine theiridentity states. We also present a heuristic user simulator based on problemanalysis to evaluate our method. Experiments have shown that the trainabledialogue system can effectively detect fraudsters, and achieve higherrecognition accuracy compared with rule-based systems. Furthermore, our learneddialogue strategies are interpretable and flexible, which can help promotereal-world applications.

Are You for Real? Detecting Identity Fraud via Dialogue Interactions