Fraud detection remains a challenging task due to the complex and deceptive nature of fraudulent activities. Current approaches primarily concentrate on learning only one perspective of the graph: either the topological structure of the graph or the attributes of individual nodes. However, we conduct empirical studies to reveal that these two types of features, while nearly orthogonal, are each independently effective. As a result, previous methods can not fully capture the comprehensive characteristics of the fraud graph. To address this dilemma, we present a novel framework called Relation-Aware GNN with transFormer~(RAGFormer) which simultaneously embeds both semantic and topological features into a target node. The simple yet effective network consists of a semantic encoder, a topology encoder, and an attention fusion module. The semantic encoder utilizes Transformer to learn semantic features and node interactions across different relations. We introduce Relation-Aware GNN as the topology encoder to learn topological features and node interactions within each relation. These two complementary features are interleaved through an attention fusion module to support prediction by both orthogonal features. Extensive experiments on two popular public datasets demonstrate that RAGFormer achieves state-of-the-art performance. The significant improvement of RAGFormer in an industrial credit card fraud detection dataset further validates the applicability of our method in real-world business scenarios.

Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks(GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform poorly due to their assumption of homophily. In addition, due to the existence of heterophily and class imbalance problem, the existing models do not fully utilize the precious node label information. To address the above issues, this paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively. The first module starts from the perspective of the spectral domain, and solves the heterophily problem to a certain extent. Specifically, it divides the spectrum into multiple mixed frequency bands according to the correlation between spectrum energy distribution and heterophily. Then in order to make full use of the node label information, a local environmental constraint module is adaptively designed. The comprehensive experimental results on four real-world fraud detection datasets show that SEC-GFD outperforms other competitive graph-based fraud detectors.

Node classification is a substantial problem in graph-based fraud detection. Many existing works adopt Graph Neural Networks (GNNs) to enhance fraud detectors. While promising, currently most GNN-based fraud detectors fail to generalize to the low homophily setting. Besides, label utilization has been proved to be significant factor for node classification problem. But we find they are less effective in fraud detection tasks due to the low homophily in graphs. In this work, we propose GAGA, a novel Group AGgregation enhanced TrAnsformer, to tackle the above challenges. Specifically, the group aggregation provides a portable method to cope with the low homophily issue. Such an aggregation explicitly integrates the label information to generate distinguishable neighborhood information. Along with group aggregation, an attempt towards end-to-end trainable group encoding is proposed which augments the original feature space with the class labels. Meanwhile, we devise two additional learnable encodings to recognize the structural and relational context. Then, we combine the group aggregation and the learnable encodings into a Transformer encoder to capture the semantic information. Experimental results clearly show that GAGA outperforms other competitive graph-based fraud detectors by up to 24.39% on two trending public datasets and a real-world industrial dataset from Anonymous. Even more, the group aggregation is demonstrated to outperform other label utilization methods (e.g., C&S, BoT/UniMP) in the low homophily setting.

Credit card fraud is a problem continuously faced by financial institutions and their customers, which is mitigated by fraud detection systems. However, these systems require the use of sensitive customer transaction data, which introduces both a lack of privacy for the customer and a data breach vulnerability to the card provider. This paper proposes a system for private fraud detection on encrypted transactions using homomorphic encryption. Two models, XGBoost and a feedforward classifier neural network, are trained as fraud detectors on plaintext data. They are then converted to models which use homomorphic encryption for private inference. Latency, storage, and detection results are discussed, along with use cases and feasibility of deployment. The XGBoost model has better performance, with an encrypted inference as low as 6ms, compared to 296ms for the neural network. However, the neural network implementation may still be preferred, as it is simpler to deploy securely. A codebase for the system is also provided, for simulation and further development.

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Unlike general-purpose machine learning (ML) packages, Amazon Fraud Detector is designed specifically to detect fraud. Amazon Fraud Detector combines your data, the latest in ML science, and more than 20 years of fraud detection experience from Amazon.com and AWS to build ML models tailor-made to detect fraud in your business. After you train a fraud detection model that is customized to your business, you create rules to interpret the model's outputs and create a detector to contain both the model and rules. You can then evaluate online activities for fraud in real time by calling your detector through the GetEventPrediction API and passing details about a single event in each request.

Online fraud is estimated to be costing businesses more than £3billion a year, according to the FBI's Internet Crime Report 2019. Excluding the United States, the United Kingdom is by far the worst affected country by number of victims. At Inawisdom, our post-fraud analyses have identified several patterns. The most common include using a common IP address or similar data in fraudulent accounts, such as email domains. In other cases, fraudsters fake the entered country, residential status, or work status when applying for accounts.

Amazon Web Services (AWS) has announced the general availability of Fraud Detector, a machine learning-powered service that helps organisations to tackle fraudulent activity. First launched at Amazon Re:invent last December, Fraud Detector uses the same technology that Amazon employs to fight fraudulent activity on its e-commerce marketplace. The tool requires no machine learning expertise, according to AWS, with Fraud Detector providing a selection of ready-made fraud detection AI templates that cover different use cases. To train their model, organisations simply upload historical data covering both fraudulent and legitimate transactions to AWS S3. Businesses with more advanced requirements can use their own models with the service using an integration with SageMaker, Amazon's managed AI platform.

Amazon is leveraging machine learning to fight fraud, audit code, transcribe calls, and index enterprise data. Today during a keynote at its Amazon Web Services (AWS) re:Invent 2019 conference in Las Vegas, the tech giant debuted Amazon Fraud Detector, a fully managed service that detects anomalies in transactions, and CodeGuru, which automates code review while identifying the most "expensive" lines of code. And those are just the tip of the iceberg. With Fraud Detector (in preview), AWS customers provide email addresses, IP addressees, and other historical transaction and account registration data, along with markers indicating which transactions are fraudulent and which are legitimate. Amazon takes that information and uses algorithms -- along with data detectors developed on the consumer business of Amazon's business -- to build bespoke models that recognize things like potentially malicious email domains and IP address formation.