VecAug：利用队列增强揭示伪装欺诈以提升检测性能
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection
AI总结：
本文提出队列增强学习框架VecAug，通过向量预热自动识别队列、注意力聚合增强表示及标签感知邻居分离机制，提升欺诈检测模型的鲁棒性与泛化能力，在三个数据集上显著优于现有方法。
摘要：
欺诈检测是一项具有挑战性的任务，其特点是欺诈模式不断演变且标注数据稀缺。现有方法主要依赖基于图或基于序列的方法。基于图的方法通过共享实体连接用户以捕获结构信息，但它们容易受到欺诈者的攻击，因为欺诈者可以破坏或操纵这些连接。相比之下，基于序列的方法分析用户的行为模式，对篡改具有鲁棒性，但忽略了相似用户之间的交互。受留存分析和医疗保健中队列分析的启发，本文提出了VecAug，一种新颖的队列增强学习框架，通过利用个性化队列信息增强目标用户的表示学习来应对这些挑战。为此，我们首先提出了一种向量预热技术用于自动队列识别，该技术为每个目标用户检索任务特定的队列。然后，为了充分利用队列信息，我们引入了一种注意力队列聚合技术来增强目标用户表示。为了提高这种队列增强的鲁棒性，我们还提出了一种新颖的标签感知队列邻居分离机制，以拉开负队列邻居的距离并校准聚合后的队列信息。通过将队列信息与目标用户表示相整合，VecAug增强了被增强模型的建模能力和泛化能力。我们的框架具有灵活性，可以无缝集成到现有的欺诈检测模型中。我们在电子商务平台上部署了该框架，并在三个欺诈检测数据集上进行了评估，结果表明VecAug将基础模型的检测性能在AUC上最多提高了2.48%，在R@P$_{0.9}$上最多提高了22.5%，显著优于最先进的方法。
英文摘要：
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 R@P$_{0.9}$, outperforming state-of-the-art methods significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
