ABSTRACT
The outbreak of COVID-19 burgeons newborn services on online platforms and simultaneously buoys multifarious online fraud activities. Due to the rapid technological and commercial innovation that opens up an ever-expanding set of products, the insufficient labeling data renders existing supervised or semi-supervised fraud detection models ineffective in these emerging services. However, the ever accumulated user behavioral data on online platforms might be helpful in improving the performance of fraud detection on newborn services. To this end, in this paper, we propose to pre-train user behavior sequences, which consist of orderly arranged actions, from the large-scale unlabeled data sources for online fraud detection. Recent studies illustrate accurate extraction of user intentions∼(formed by consecutive actions) in behavioral sequences can propel improvements in the performance of online fraud detection. By anatomizing the characteristic of online fraud activities, we devise a model named UB-PTM that learns knowledge of fraud activities by three agent tasks at different granularities, i.e., action, intention, and sequence levels, from large-scale unlabeled data. Extensive experiments on three downstream transaction and user-level online fraud detection tasks demonstrate that our UB-PTM is able to outperform the state-of-the-art designing for specific tasks. © 2022 ACM.