Sequence As Genes: An User Behavior Modeling Framework for Fraud Transaction Detection in E-commerce

With the explosive growth of e-commerce, detecting fraudulent transactions in real-world scenarios is becoming increasingly important for e-commerce platforms. Recently, several supervised approaches have been proposed to use user behavior sequences, which record the user's track on platforms and contain rich information for fraud transaction detection. Nevertheless, these methods always suffer from the scarcity of labeled data in real-world scenarios. The recent remarkable pre-training methods in Natural Language Processing (NLP) and Computer Vision (CV) domains offered glimmers of light. However, user behavior sequences differ intrinsically from text, images, and videos. In this paper, we propose a novel and general user behavior pre-training framework, named Sequence As GEnes (SAGE), which provides a new perspective for user behavior modeling. Following the inspiration of treating sequences as genes, we carefully designed the user behavior data organization paradigm and pre-training scheme. Specifically, we propose an efficient data organization paradigm inspired by the nature of DNA expression, which decouples the length of behavior sequences and the corresponding time spans. Also inspired by the natural mechanisms in genetics, we propose two pre-training tasks, namely sequential mutation and sequential recombination, to improve the robustness and consistency of user behavior representations in complicated real-world scenes. Extensive experiments on four differentiated fraud transaction detection real scenarios demonstrate the effectiveness of our proposed framework.