Multi-task convolutional neural network for behavior sequence embedding modeling

Abstract

Some aspects of the present technology relate to performing fraud detection on online transaction platforms through user behavior sequence data. A multi-task convolutional neural network (MTCNN) model is used to predict, in real-time, whether user behavior sequence data is indicative of fraudulent activity. To perform fraud detection in such configurations, a one-layer convolutional neural network architecture with multi-range kernels is employed. The MTCNN model receives a sequence of page browsing signals corresponding to a buyer. Each page browsing signal corresponds to a position in the sequence. One or more portions of the page browsing signals are selected. Each of the one or more portions of the page browsing signals and the corresponding position are embedded in one or more sequence embeddings. A fraud risk for each of the one or more sequence embeddings is predicted utilizing the MTCNN model.