  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, namedSemiGNN 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.
