Are You for Real? Detecting Identity Fraud via Dialogue Interactions

Weikang Wang, Jiajun Zhang, Qian Li, Chengqing Zong, Zhifei Li

Abstract
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.
Anthology ID:
D19-1185
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1762–1771
Language:
URL:
https://aclanthology.org/D19-1185
DOI:
10.18653/v1/D19-1185
Bibkey:
Cite (ACL):
Weikang Wang, Jiajun Zhang, Qian Li, Chengqing Zong, and Zhifei Li. 2019. Are You for Real? Detecting Identity Fraud via Dialogue Interactions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1762–1771, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Are You for Real? Detecting Identity Fraud via Dialogue Interactions (Wang et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/improve-issue-templates/D19-1185.pdf
Attachment:
 D19-1185.Attachment.zip
Code
 Leechikara/Dialogue-Based-Anti-Fraud
PDF Search Code Attachment