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.
Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention
Columbia University（哥伦比亚大学）；University of Michigan（密歇根大学）；Wake Forest University（维克森林大学）；University of Minnesota（明尼苏达大学）；The University of Chicago（芝加哥大学）；Northeastern University（东北大学）
The University of Tokyo（东京大学）；National Institute of Advanced Industrial Science and Technology (AIST)（国立先进工业科学技术研究所（AIST））
While transactions with cryptocurrencies such as Ethereum are becoming more prevalent, fraud and other criminal transactions are not uncommon. Graph analysis algorithms and machine learning techniques detect suspicious transactions that lead to phishing in large transaction networks. Many graph neural network (GNN) models have been proposed to apply deep learning techniques to graph structures. Although there is research on phishing detection using GNN models in the Ethereum transaction network, models that address the scale of the number of vertices and edges and the imbalance of labels have …
Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patterns
Department of Electrical Engineering（电气工程系）；Sharif University of Technology（谢里夫技术大学）；Department of Electrical and Electronic Engineering（电气与电子工程系）；Shahed University（沙赫德大学）
Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow
School of Engineering（工程学院）；Department of Mechanical and Aerospace Engineering（机械与航空航天工程系）；University of Houston（休斯顿大学）；School of Electrical and Computer Engineering（电气与计算机工程学院）；Cornell University（康奈尔大学）；Fu Foundation School of Engineering and Applied Science（工程与应用科学学院）；Columbia University in the City of New York（纽约市哥伦比亚大学）
Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces. In order to leverage graph networks to decrease fraud rate efficiently and guarantee the information flow passed through neighbors only from the past of the checkouts, we first present a novel Directed Dynamic Snapshot (DDS) linkage design for graph construction and a Lambda Neural Networks (LNN) architecture for effective inference with Graph Neural Networks embeddings. Experiments show that our LNN on DDS graph, outperforms baseline models significantly and is computational efficient for…
LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding
Yantai Research Institute, Harbin Engineering University（哈尔滨工程大学烟台研究院）；Hangzhou Research Institute, Xidian University（西安电子科技大学杭州研究院）；College of Computer Science, Zhejiang University（浙江大学计算机学院）
DynBERG: Dynamic BERT-based Graph neural network for financial fraud detection
Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney, Australia（过渡人工智能研究组，数学与统计学学院）；Centre for Artificial Intelligence and Innovation, Pingala Institute, Sydney, Australia（人工智能与创新中心，Pingala研究所）；Department of Economics and Finance（经济学与金融系）
Shanghai Jiao Tong University（上海交通大学）；eBay Inc.（易趣公司）；Swiss Federal Institute of Technology in Zurich (ETHZ)（苏黎世联邦理工学院）；Wuhan University（武汉大学）
Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph. However, two challenges arise in the implementation of GNNs in production. First, future information in a dynamic graph should not be considered in message passing to predict the past. Second, the latency of graph query and GNN model inference is…
Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；School of Computer Science and Technology, Donghua University（东华大学计算机科学与技术学院）
Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation
Australian Artificial Intelligence Institute, University of Technology Sydney（悉尼科技大学澳大利亚人工智能研究所）；Tongji University（同济大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Tencent Jarvis Laboratory（腾讯贾维斯实验室）
Detection of a Fraud transaction on credit cards became one of the major problems for financial institutions, organizations and companies. As the global financial system is highly connected to non-cash transactions and online operations fraud makers invent more effective ways to access customers' finances. The main problem in credit card fraud detection is that the number of fraud transactions is significantly lower than genuine ones. The aim of the paper is to implement new techniques, which contains of under-sampling algorithms, K-nearest Neighbor Algorithm (KNN) and Deep Neural Network (KNN…
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…
At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions. Despite the variety of different models for deep learning on graphs, few approaches have been proposed for dealing with graphs that are both heterogeneous and dynamic. In this paper, we propose DyHGN (Dynamic Heterogeneous Graph Neural Network) and its variants to capture both temporal and heterogeneous information. We first construct dynamic heterogeneous graphs from registration and transaction data from eBay. Then, we build…
Research Institute of Electronic Payment, China UnionPay（中国银联电子支付研究院）；Fudan University（复旦大学）；Intel Corporation（英特尔公司）；ZhongAn Information Technology Services Co.,Ltd.（众安信息技术服务有限公司）
Rapid growth of modern technologies such as internet and mobile computing are bringing dramatically increased e-commerce payments, as well as the explosion in transaction fraud. Meanwhile, fraudsters are continually refining their tricks, making rule-based fraud detection systems difficult to handle the ever-changing fraud patterns. Many data mining and artificial intelligence methods have been proposed for identifying small anomalies in large transaction data sets, increasing detecting efficiency to some extent. Nevertheless, there is always a contradiction that most methods are irrelevant to…
Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；The University of Edinburgh（爱丁堡大学）
In this study, we employ Generative Adversarial Networks as an oversampling method to generate artificial data to assist with the classification of credit card fraudulent transactions. GANs is a generative model based on the idea of game theory, in which a generator G and a discriminator D are trying to outsmart each other. The objective of the generator is to confuse the discriminator. The objective of the discriminator is to distinguish the instances coming from the generator and the instances coming from the original dataset. By training GANs on a set of credit card fraudulent transactions,…
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspective…
University of Duisburg-Essen（杜伊斯堡-埃森大学）；University of Passau（帕绍大学）；Worldline（Worldline公司）
Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively and avoid further frauds. Since human verification is often necessary and the payment processing time is limited, the second task manages the largest volume of transactions. In the literature, fraud detection challenges and algorithms performance are widely studied but the very formulation of the problem is never disrupted: it aims at predicting if a transaction is fraudulent based on its characteristics and the past…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Electronic Arts（艺电公司）；Tsinghua University（清华大学）
On electronic game platforms, different payment transactions have different levels of risk. Risk is generally higher for digital goods in e-commerce. However, it differs based on product and its popularity, the offer type (packaged game, virtual currency to a game or subscription service), storefront and geography. Existing fraud policies and models make decisions independently for each transaction based on transaction attributes, payment velocities, user characteristics, and other relevant information. However, suspicious transactions may still evade detection and hence we propose a broad lea…
Heterogeneous Graph Auto-Encoder for CreditCard Fraud Detection
Department of Computer Science and Engineering（计算机科学与工程系）；Dibrugarh University Institute of Engineering and Technology（迪布加尔大学工程与技术学院）；Department of Electronic and Communication Engineering（电子与通信工程系）
Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization
Brandeis University（布雷纳大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Georgetown University（乔治城大学）；Columbia University（哥伦比亚大学）；Stevens Institute of Technology（史蒂文斯理工学院）
Université Libre de Bruxelles（布鲁塞尔自由大学）；Worldline（世界线）
Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an …
John von Neumann Institute（约翰·冯·诺依曼研究所）；Thu Dau Mot University（守德莫特大学）
The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised learning techniques and supervised learning techniques. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Exp…
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspectives, namely (i) The sequence contains or doesn't contain a fraud (ii) The sequence is obtained by fixing the card-holder or the payment terminal (iii) It is a sequence of spent amount or of elapsed time between the current and previous transactions. Combinations of the three bin…
In e-commerce industry, user behavior sequence data has been widely used in many business units such as search and merchandising to improve their products. However, it is rarely used in financial services not only due to its 3V characteristics - i.e. Volume, Velocity and Variety - but also due to its unstructured nature. In this paper, we propose a Financial Service scenario Deep learning based Behavior data representation method for Clustering (FinDeepBehaviorCluster) to detect fraudulent transactions. To utilize the behavior sequence data, we treat click stream data as event sequence, use ti…
The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given rise to cybercriminals and fraudsters who are constantly looking for vulnerabilities in these systems by deploying increasingly sophisticated fraud attacks. A typical fraud detection system employs standard supervised learning methods where the focus is on maximizing the fraud recall rate. However, we argue that such a formulation can lead to sub-optimal solutions. The design requirements for these fraud models require…
A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions
Credit cards play an exploding role in modern economies. Its popularity and ubiquity have created a fertile ground for fraud, assisted by the cross boarder reach and instantaneous confirmation. While transactions are growing, the fraud percentages are also on the rise as well as the true cost of a dollar fraud. Volume of transactions, uniqueness of frauds and ingenuity of the fraudster are main challenges in detecting frauds. The advent of machine learning, artificial intelligence and big data has opened up new tools in the fight against frauds. Given past transactions, a machine learning algo…
University of Applied Sciences and Arts, Western Switzerland (HES-SO)（瑞士西部应用科学与艺术大学）；School of Engineering and Business Vaud (HEIG-Vd)（沃州工程与商业学院）；NetGuardians SA（NetGuardians 公司）
This paper addresses the problem of unsupervised approach of credit card fraud detection in unbalanced dataset using the ARIMA model. The ARIMA model is fitted on the regular spending behaviour of the customer and is used to detect fraud if some deviations or discrepancies appear. Our model is applied to credit card datasets and is compared to 4 anomaly detection approaches such as K-Means, Box-Plot, Local Outlier Factor and Isolation Forest. The results show that the ARIMA model presents a better detecting power than the benchmark models.
With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security. In this work, we present a novel method for detecting fraud transactions by leveraging patterns from both users' static profiles and users' dynamic behaviors in a unified framework. To address and explore the information of users' behaviors in continuous time spaces, we propose to use \emph{time attention based recurrent layers} to embed the detailed information of the time interval, such as the durations of specif…
University of Bridgeport（桥港大学）；University of the Potomac（波托马克大学）；Westcliff University（韦斯特克利夫大学）；Gannon University（甘农大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Washington University of Science and Technology（华盛顿科学技术大学）；Trine University（特莱恩大学）
Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning
Columbia University（哥伦比亚大学）；New York University（纽约大学）；University of Pennsylvania（宾夕法尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Michigan（密歇根大学）
Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common pr…
