8 research outputs found
xFraud: Explainable Fraud Transaction Detection
At online retail platforms, it is crucial to actively detect the risks of
transactions to improve customer experience and minimize financial loss. In
this work, we propose xFraud, an explainable fraud transaction prediction
framework which is mainly composed of a detector and an explainer. The xFraud
detector can effectively and efficiently predict the legitimacy of incoming
transactions. Specifically, it utilizes a heterogeneous graph neural network to
learn expressive representations from the informative heterogeneously typed
entities in the transaction logs. The explainer in xFraud can generate
meaningful and human-understandable explanations from graphs to facilitate
further processes in the business unit. In our experiments with xFraud on real
transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud
is able to outperform various baseline models in many evaluation metrics while
remaining scalable in distributed settings. In addition, we show that xFraud
explainer can generate reasonable explanations to significantly assist the
business analysis via both quantitative and qualitative evaluations.Comment: This is the extended version of a full paper to appear in PVLDB 15
(3) (VLDB 2022
Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism
As one of the major frauds in financial services, cash-out fraud is that users pursue cash gains with illegal or insincere means. Conventional solutions for the cash-out user detection are to perform subtle feature engineering for each user and then apply a classifier, such as GDBT and Neural Network. However, users in financial services have rich interaction relations, which are seldom fully exploited by conventional solutions. In this paper, with the real datasets in Ant Credit Pay of Ant Financial Services Group, we first study the cashout user detection problem and propose a novel hierarchical attention mechanism based cash-out user detection model, called HACUD. Specifically, we model different types of objects and their rich attributes and interaction relations in the scenario of credit payment service with an Attributed Heterogeneous Information Network (AHIN). The HACUD model enhances feature representation of objects through meta-path based neighbors exploiting different aspects of structure information in AHIN. Furthermore, a hierarchical attention mechanism is elaborately designed to model user’s preferences towards attributes and meta-paths. Experimental results on two real datasets show that the HACUD outperforms the state-of-the-art methods
Deep learning structure for directed graph data
Deep learning structures have achieved outstanding success in many different domains. Existing research works have proposed and presented many state-of-the-art deep neural networks to solve different learning tasks in various research fields such as speech processing and image recognition. Graph neural networks (GNNs) are considered as a type of deep neural network and their numerical representation from the graph does improve the performance of networks. In the real-world cases, data
is not only in the form of simple graph, but also they could contain direction information in the graph resulting in the so-called directed
graph data.
This thesis will introduce and explain the first attempt in this domain to apply Singular Value Decomposition (SVD) on adjacency matrix for
graph convolutional neural networks and propose SVD-GCN. This thesis also utilizes the framelet decomposition to help better filter the graph
signals, thus to improve novel structure’s performance in node classification task and to enhance the robustness of the model when
encountering high-level noise attack. The thesis also applies the new model on link prediction tasks. All the experimental results demonstrate
SVD-GCN’s outstanding performances in both node-level and edgelevel learning tasks