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.

本研究提出一种新型的交互式对话系统，包括知识图谱构造器和结构化对话管理模块，用于贷款申请中的身份欺诈检测。实验证明，该系统可以有效地检测欺诈者，并在识别准确性方面优于基于规则的系统。此外，该系统的学习对话策略具有可解释性和灵活性，有助于推广实际应用。