Are You for Real? Detecting Identity Fraud via Dialogue Interactions
This addresses identity fraud detection for the financial industry, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles identity fraud detection in loan applications by proposing an interactive dialogue system that uses knowledge graphs and structured dialogue management to question applicants, achieving higher recognition accuracy than rule-based systems.
Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules. One is the knowledge graph (KG) constructor organizing the personal information for each loan applicant. The other is structured dialogue management that can dynamically generate a series of questions based on the personal KG to ask the applicants and determine their identity states. We also present a heuristic user simulator based on problem analysis to evaluate our method. Experiments have shown that the trainable dialogue system can effectively detect fraudsters, and achieve higher recognition accuracy compared with rule-based systems. Furthermore, our learned dialogue strategies are interpretable and flexible, which can help promote real-world applications.