A SemiSupervised Graph Attentive Network for Financial Fraud Detection 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 rulebased 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 semisupervised attentive graph neural network, named SemiGNN to utilize the multiview 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 thirdparty 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 stateoftheart methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.

Loan Recommendation in P2P Lending Investment Networks: A Hybrid Graph Convolution Approach Low successful rate of loan money has become a significant challenge for P2P Lending platforms. In this paper, we propose a hybrid deep learning algorithm combining strengths of both supervised graph convolution network and unsupervised community discovery, to accurately match the loan requirements and investing lenders on P2P Lending platforms. Our hybrid deep architecture learns predictive node embedding from both local neighborhood information and global structural information through complex investment networks with minimal information loss. We evaluate our method on largescale dataset collected from a realworld P2P platform. Compared with strong baselines, our proposed method provides optimal loan recommendation performance, generates efficient solutions for xe2x80x9cColdStartxe2x80x9d problem and features fast computing speed.