A Semi-Supervised Graph Attentive Network for Financial Fraud Detection

By Daixin Wang, Y. Qi, J. Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang. Industrial Conference on Data Mining, 2019

468 citations recorded by the source corpus.

With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection. To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semi-supervised attentive graph neural network, named SemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest third-party online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/0353c467cb380025d8ef927996436b8e9cfedccc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zi-Chao Gao
2. Qing He
3. Hui Xiong
4. Jia Wu
5. Quan Z. Sheng
6. Chuan Zhou
7. Jiajin Li
8. Shan Xue

Citations in the corpus

• Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (2021, methodology): “Semi-GNN [39] is a semi-supervised GNN model with hierarchical attention mechanism for explainable fraud prediction.”
• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, methodology): “com/FraudDetection/FRAUDRE SemiGNN [111] Python Tensorflow Static Attributed Graph https://github.”
• Rethinking Graph Neural Networks for Anomaly Detection (2022, methodology): “That is, (1) applying attention mechanisms to correlate different neighbors through various views (Wang et al., 2019a; Cui et al., 2020; Liu et al., 2021a), (2) using the resampling strategy to aggregate neighborhood information selectively (Dou et al., 2020; Liu et al., 2020; 2021b), (3) designing…”