Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions. This paper explores the application of Generative Adversarial Networks (GANs) in fraud detection, comparing their advantages with traditional methods. GANs, a type of Artificial Neural Network (ANN), have shown promise in modeling complex data distributions, making them effective tools for anomaly detection. The paper systematically describes the principles of GANs and their derivative models, emphasizing their application in fraud detection across different datasets. And by building a collection of adversarial verification graphs, we will effectively prevent fraud caused by bots or automated systems and ensure that the users in the transaction are real. The objective of the experiment is to design and implement a fake face verification code and fraud detection system based on Generative Adversarial network (GANs) algorithm to enhance the security of the transaction process.The study demonstrates the potential of GANs in enhancing transaction security through deep learning techniques.
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…
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）
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…
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…
City, University of London（伦敦城市大学）；BetBuddy Playtech Plc（贝特巴迪博彩科技公共有限公司）
Detecting money laundering in gambling is becoming increasingly challenging for the gambling industry as consumers migrate to online channels. Whilst increasingly stringent regulations have been applied over the years to prevent money laundering in gambling, despite this, online gambling is still a channel for criminals to spend proceeds from crime. Complementing online gambling's growth more concerns are raised to its effects compared with gambling in traditional, physical formats, as it might introduce higher levels of problem gambling or fraudulent behaviour due to its nature of immediate i…
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…
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.
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）
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…
Credit card fraud has emerged as major problem in the electronic payment sector. In this survey, we study data-driven credit card fraud detection particularities and several machine learning methods to address each of its intricate challenges with the goal to identify fraudulent transactions that have been issued illegitimately on behalf of the rightful card owner. In particular, we first characterize a typical credit card detection task: the dataset and its attributes, the metric choice along with some methods to handle such unbalanced datasets. These questions are the entry point of every cr…
Secure Energy Transactions Using Blockchain Leveraging AI for Fraud Detection and Energy Market Stability
Southern New Hampshire University（南新罕布什尔大学）；Mercy University（默西大学）；International American University（国际美国大学）；Washington University of Science and Technology（华盛顿科学技术大学）；Gannon University（甘农大学）；Farmingdale State College（法明代尔州立学院）
Credit card has become popular mode of payment for both online and offline purchase, which leads to increasing daily fraud transactions. An Efficient fraud detection methodology is therefore essential to maintain the reliability of the payment system. In this study, we perform a comparison study of credit card fraud detection by using various supervised and unsupervised approaches. Specifically, 6 supervised classification models, i.e., Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), as…
The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the development of technology and the rise in fraud cases. This paper implements the random forest (RF) algorithm to solve the issue in the hand. A dataset of credit card transactions was used in this study. The main problem when dealing with credit card fraud detection is the imbalanced dataset in which most of the transaction are non-fraud ones. To overcome the pro…
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…
Centre for Artificial Intelligence and Machine Learning Institute for Development and Research in Banking Technology（人工智能与机器学习中心，银行技术发展与研究院）
Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insurance and auto insurance among other things. In either of the categories, the fraud detection techniques should be designed in such a way that they capture as many fraudulent transactions as possible. Owing to the rarity of fraudulent transactions, in this paper, we propose a chao…
For the highly imbalanced credit card fraud detection problem, most existing methods either use data augmentation methods or conventional machine learning models, while neural network-based anomaly detection approaches are lacking. Furthermore, few studies have employed AI interpretability tools to investigate the feature importance of transaction data, which is crucial for the black-box fraud detection module. Considering these two points together, we propose a novel anomaly detection framework for credit card fraud detection as well as a model-explaining module responsible for prediction exp…
National Institute of Technology Kurukshetra（库鲁克舍特拉国立理工学院）；National Institute of Technology Tiruchirappalli（蒂鲁吉拉帕利国立理工学院）
As the world is rapidly moving towards digitization and money transactions are becoming cashless, the use of credit cards has rapidly increased. The fraud activities associated with it have also been increasing which leads to a huge loss to the financial institutions. Therefore, we need to analyze and detect the fraudulent transaction from the non-fraudulent ones. In this paper, we present a comprehensive review of various methods used to detect credit card fraud. These methodologies include Hidden Markov Model, Decision Trees, Logistic Regression, Support Vector Machines (SVM), Genetic algori…
Fraud detection is essential in financial services, with the potential of greatly reducing criminal activities and saving considerable resources for businesses and customers. We address online fraud detection, which consists of classifying incoming transactions as either legitimate or fraudulent in real-time. Modern fraud detection systems consist of a machine learning model and rules defined by human experts. Often, the rules performance degrades over time due to concept drift, especially of adversarial nature. Furthermore, they can be costly to maintain, either because they are computational…
Credit Card Fraud Detection in the Nigerian Financial Sector: A Comparison of Unsupervised TensorFlow-Based Anomaly Detection Techniques, Autoencoders and PCA Algorithm
Enugu State University of Science and Technology（埃努古州立科技大学）
Posterior Regimes and Latent Deception: Variational Bayesian Inference in Hidden Markov Models for Sequential Fraud Detection in Financial Transactions
University of Stirling（斯特灵大学）；University of Nottingham（诺丁汉大学）
Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising fraud percentages there is growing interest in finding appropriate machine learning classifiers for detection. However, fraud data sets are diverse and exhibit inconsistent characteristics. As a result, a model effective on a given data set is not guaranteed to perform on another. Further, the possibility of temporal drift in data patterns and characteristics…
Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed learning technique that uses K-means preprocessing before trained classification to the problem at hand. Next, we introduce an adapted detector ensemble technique that uses OR-logic algorithm aggregation to enhance the detection rate. Then, both strategies are deployed in tandem in numerical simulations using real-world transactions data. We observed from simulat…
In e-commerce industry, graph neural network methods are the new trends for transaction risk modeling.The power of graph algorithms lie in the capability to catch transaction linking network information, which is very hard to be captured by other algorithms.However, in most existing approaches, transaction or user connections are defined by hard link strategies on shared properties, such as same credit card, same device, same ip address, same shipping address, etc. Those types of strategies will result in sparse linkages by entities with strong identification characteristics (ie. device) and o…
Institute for Development and Research in Banking Technology（银行业技术发展与研究所）；Center for Development of Advanced Computing（高级计算发展中心）
Gaining the trust of customers and providing them empathy are very critical in the financial domain. Frequent occurrence of fraudulent activities affects these two factors. Hence, financial organizations and banks must take utmost care to mitigate them. Among them, ATM fraudulent transaction is a common problem faced by banks. There following are the critical challenges involved in fraud datasets: the dataset is highly imbalanced, the fraud pattern is changing, etc. Owing to the rarity of fraudulent activities, Fraud detection can be formulated as either a binary classification problem or One …
