POSTER: Practical Fraud Transaction Prediction

Nowadays, online payment systems play more and more important roles in people's daily lives. A key component of these systems is to detect and prevent fraud transactions. In industrial practice, such a task is separated into two phases: 1) mining evidential features to describe users, 2) building an effective model based on these features. Generally speaking, the most popular fraud transaction detection systems use elaborately designed features to build tree based models, sometimes a subsequent linear model is added to improve the behaviour. However, the designed features usually contains only static features, while dynamic features are not considered. In addition, the subsequent model can only learn a linear combination, which may always be unsatisfactory. To address these issues, we present a systematic method, which extracts not only users' static features but also dynamic features based on their recent behaviors. Moreover, N-GRAM model is employed to handle the dynamic features so that time series information is addressed. Based on the extracted features, a tree based model is applied and the outputs of it are regarded as new generated feature representations, which will be further inputted into a Deep Neural Network (DNN) to learn the complex relationships and form the final classification model. Extensive experiments show that our proposed model (with both static and dynamic features) significantly outperforms the existing methods.