  Fraud detection presents a challenging task characterized by ever-evolving
fraud patterns and scarce labeled data. Existing methods predominantly rely on
graph-based or sequence-based approaches. While graph-based approaches connect
users through shared entities to capture structural information, they remain
vulnerable to fraudsters who can disrupt or manipulate these connections. In
contrast, sequence-based approaches analyze users' behavioral patterns,
offering robustness against tampering but overlooking the interactions between
similar users. Inspired by cohort analysis in retention and healthcare, this
paper introduces VecAug, a novel cohort-augmented learning framework that
addresses these challenges by enhancing the representation learning of target
users with personalized cohort information. To this end, we first propose a
vector burn-in technique for automatic cohort identification, which retrieves a
task-specific cohort for each target user. Then, to fully exploit the cohort
information, we introduce an attentive cohort aggregation technique for
augmenting target user representations. To improve the robustness of such
cohort augmentation, we also propose a novel label-aware cohort neighbor
separation mechanism to distance negative cohort neighbors and calibrate the
aggregated cohort information. By integrating this cohort information with
target user representations, VecAug enhances the modeling capacity and
generalization capabilities of the model to be augmented. Our framework is
flexible and can be seamlessly integrated with existing fraud detection models.
We deploy our framework on e-commerce platforms and evaluate it on three fraud
detection datasets, and results show that VecAug improves the detection
performance of base models by up to 2.48\% in AUC and 22.5\% in<EMAIL_ADDRESS>outperforming state-of-the-art methods significantly.
