Many machine learning methods have been proposed to achieve accurate
transaction fraud detection, which is essential to the financial security of
individuals and banks. However, most existing methods leverage original
features only or require manual feature engineering. They lack the ability to
learn discriminative representations from transaction data. Moreover, criminals
often commit fraud by imitating cardholders' behaviors, which causes the poor
performance of existing detection models. In this paper, we propose an Adaptive
Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns
discriminative representations to improve the performance of transaction fraud
detection. A neighbor sampling strategy is performed to filter noisy nodes and
supplement information for fraudulent nodes. Specifically, we leverage cosine
similarity and edge weights to adaptively select neighbors with similar
behavior patterns for target nodes and then find multi-hop neighbors for
fraudulent nodes. A neighbor diversity metric is designed by calculating the
entropy among neighbors to tackle the camouflage issue of fraudsters and
explicitly alleviate the over-smoothing phenomena. Extensive experiments on
three real financial datasets demonstrate that the proposed method ASA-GNN
outperforms state-of-the-art ones.

本文提出了一种基于自适应采样和聚合的图神经网络 (ASA-GNN)，通过邻居采样策略和邻居多样性度量，在交易数据中学习判别表示以提高欺诈检测的性能，实验结果表明该方法优于现有的其他方法。

利用自适应图神经网络进行交易欺诈检测

Transaction Fraud Detection via an Adaptive Graph Neural Network

How to obtain informative representations of transactions and then perform
the identification of fraudulent transactions is a crucial part of ensuring
financial security. Recent studies apply Graph Neural Networks (GNNs) to the
transaction fraud detection problem. Nevertheless, they encounter challenges in
effectively learning spatial-temporal information due to structural
limitations. Moreover, few prior GNN-based detectors have recognized the
significance of incorporating global information, which encompasses similar
behavioral patterns and offers valuable insights for discriminative
representation learning. Therefore, we propose a novel heterogeneous graph
neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for
transaction fraud detection problems. Specifically, we design a temporal
encoding strategy to capture temporal dependencies and incorporate it into the
graph neural network framework, enhancing spatial-temporal information modeling
and improving expressive ability. Furthermore, we introduce a transformer
module to learn local and global information. Pairwise node-node interactions
overcome the limitation of the GNN structure and build up the interactions with
the target node and long-distance ones. Experimental results on two financial
datasets compared to general GNN models and GNN-based fraud detectors
demonstrate that our proposed method STA-GT is effective on the transaction
fraud detection task.

本文提出了一种名为 STA-GT 的异构图神经网络，通过引入一种时间编码和转换器模块，来捕获时间依赖性、识别欺诈性交易和促进时空信息建模，具有改进的表现能力。在两个金融数据集上进行的实验证明了该方法在交易欺诈检测任务上的有效性。

基于时空感知图变换器的交易欺诈检测

Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer

With the explosive growth of e-commerce and the booming of e-payment,
detecting online transaction fraud in real time has become increasingly
important to Fintech business. To tackle this problem, we introduce the TitAnt,
a transaction fraud detection system deployed in Ant Financial, one of the
largest Fintech companies in the world. The system is able to predict online
real-time transaction fraud in mere milliseconds. We present the problem
definition, feature extraction, detection methods, implementation and
deployment of the system, as well as empirical effectiveness. Extensive
experiments have been conducted on large real-world transaction data to show
the effectiveness and the efficiency of the proposed system.

TitAnt 系统是部署在全球最大的金融科技公司之一的 Ant Financial 的一种交易欺诈检测系统，能够在毫秒级别内预测在线实时交易欺诈。