Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field

Authors

  • Bingbing Xu Institute of Computing Technology, University of Chinese Academy of Sciences
  • Huawei Shen Institute of Computing Technology, University of Chinese Academy of Sciences
  • Bingjie Sun Ant Financial Services Group
  • Rong An Ant Financial Services Group
  • Qi Cao Institute of Computing Technology, University of Chinese Academy of Sciences
  • Xueqi Cheng Institute of Computing Technology, University of Chinese Academy of Sciences

Keywords:

Graph Mining, Social Network Analysis & Community, Applications, Geometric, Spatial, and Temporal Reasoning, Social Networks

Abstract

Consumer loans, i.e., loans to finance consumers to buy certain types of expenditures, is increasingly popular in e-commerce platform. Different from traditional loans with mortgage, online consumer loans only take personal credit as collateral for loans. Consequently, loan fraud detection is particularly critical for lenders to avoid economic loss. Previous methods mainly leverage applicant's attributes and historical behavior for loan fraud detection. Although these methods gain success at detecting potential charge-offs, yet they perform worse when multiple persons with various roles (e.g., sellers, intermediaries) collude to apply fraudulent loan. To combat this challenge, we consider the problem of loan fraud detection via exploiting roles of users and multi-type social relationships among users. We propose a novel Graph neural network with a Role-constrained Conditional random field, namely GRC, to learn the representation of applicants and detect loan fraud based on the learned representation. The proposed model characterizes the multiple types of relationships via self-attention mechanism and employs conditional random field to constrain users with the same role to have similar representation. We validate the proposed model through experiments in large-scale auto-loan scenario. Extensive experiments demonstrate that our model achieves state-of-the-art results in loan fraud detection on Alipay, one online credit payment service serving more than 450 million users in China.

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Published

2021-05-18

How to Cite

Xu, B., Shen, H., Sun, B., An, R., Cao, Q., & Cheng, X. (2021). Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4537-4545. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16582

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management