With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.
To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semi-supervised attentive graph neural network, namedSemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest third-party online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.
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…
HC Financial Service Group（HC金融服务集团）；HC Research（HC研究院）
Financial fraud detection is one of the core technological assets of Fintech companies. It saves tens of millions of money fro m Chinese Fintech companies since the bad loan rate is more than 10%. HC Financial Service Group is the 3rd largest company in the Chinese P2P financial market. In this paper we illustrate how we tackle the fraud detection problem at HC Financial. We utilize two powerful workhorses in the machine learning field - random forest and gradient boosting decision tree to detect fraudulent users . We demonstrate that by carefully select features and tune model parameters , we…
Columbia University（哥伦比亚大学）；University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Georgia Institute of Technology（佐治亚理工学院）
In recent years, the unprecedented growth in digital payments fueled consequential changes in fraud and financial crimes. In this new landscape, traditional fraud detection approaches such as rule-based engines have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest. Graph neural networks and emerging adaptive solutions provide compelling opportunities for the future of fraud and financial crime detection. However, implementing the graph-based solutions in financial transaction processing systems has brought numerous …
Columbia University（哥伦比亚大学）；University of Alabama（阿拉巴马大学）
The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we …
Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information…
School of Information Technology, Deakin University（迪肯大学信息技术学院）；School of Computer Science, The University of Adelaide（阿德莱德大学计算机科学学院）
Credit card frauds are at an ever-increasing rate and have become a major problem in the financial sector. Because of these frauds, card users are hesitant in making purchases and both the merchants and financial institutions bear heavy losses. Some major challenges in credit card frauds involve the availability of public data, high class imbalance in data, changing nature of frauds and the high number of false alarms. Machine learning techniques have been used to detect credit card frauds but no fraud detection systems have been able to offer great efficiency to date. Recent development of de…
A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions
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）
University of East London（东伦敦大学）；Intelligent Voice Ltd.（智能语音有限公司）；Strenuus Ltd.（Strenuus有限公司）
Detecting the elements of deception in a conversation is one of the most challenging problems for the AI community. It becomes even more difficult to design a transparent system, which is fully explainable and satisfies the need for financial and legal services to be deployed. This paper presents an approach for fraud detection in transcribed telephone conversations using linguistic features. The proposed approach exploits the syntactic and semantic information of the transcription to extract both the linguistic markers and the sentiment of the customer's response. We demonstrate the results o…
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）
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
Georgia Institute of Technology, College of Computing（佐治亚理工学院计算机学院）；Harvard University, School of Engineering and Applied Sciences（哈佛大学工程与应用科学学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University, The Fu Foundation School of Engineering and Applied Science（哥伦比亚大学傅以文工程与应用科学学院）
Due to the popularity of the Internet and smart mobile devices, more and more financial transactions and activities have been digitalized. Compared to traditional financial fraud detection strategies using credit-related features, customers are generating a large amount of unstructured behavioral data every second. In this paper, we propose an Recurrent Neural Netword (RNN) based deep-learning structure integrated with Markov Transition Field (MTF) for predicting online fraud behaviors using customer's interactions with websites or smart-phone apps as a series of states. In practice, we tested…
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…
A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
University of Melbourne（墨尔本大学）；Trinity College Dublin（都柏林三一学院）；Indian Institute of Technology（印度理工学院）；Birla Institute of Technology and Science Pilani（比拉理工学院和科学学院）；South Asian University（南亚大学）；New York University Abu Dhabi (NYUAD)（纽约大学阿布扎克分校）；NYUAD Research Institute（NYUAD研究机构）
Comprehensive Analysis of VQC for Financial Fraud Detection: A Comparative Study of Quantum Encoding Techniques and Architectural Optimizations
School of Science；Engineering, Al Akhawayn University, Ifrane, Morocco.；Engineering Sciences Laboratory,Polydisciplinary Faculty of Taza, Sidi Mohamed Ben Abdellah University, Fez, Morocco.；Signal Systems \& Components Laboratory, Faculty of Sciences；Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco.；University of Medina Saudi Arabia, Kingdom of Saudi Arabia
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）
Advanced Financial Fraud Detection Using GNN-CL Model
Columbia University（哥伦比亚大学）；Rutgers University（罗格斯大学）；College of William & Mary（威廉与玛丽学院）；Stevens Institute of Technological（史蒂文斯理工学院）；University of California, Davis（加利福尼亚大学戴维斯分校）
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）
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
Computer Science Department, College of Computers and Information Technology, Taif University（计算机科学系，计算机与信息科技学院，泰夫大学）；Department of Computer Science, Taif University（计算机科学系，泰夫大学）
International Digital Economy Academy(Futian)（国际数字经济研究院（福田））；University of Oxford（牛津大学）；Harbin Institute of Technology, ShenZhen（哈尔滨工业大学（深圳））；Xi’an Jiaotong University（西安交通大学）；City University of Hong Kong（香港城市大学）；Xi’dian University（西安电子科技大学）
Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More…
University of Bucharest（布加勒斯特大学）；The Research Institute University of Bucharest (ICUB)（布加勒斯特大学研究院（ICUB））
In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. Financial transactions, customer reviews, social media posts are all characterized by relational information. In these networks, fraudulent behaviour may appear as a distinctive graph edge, such as spam message, a node or a larger subgraph structure, such as when a group of clients engage in money laundering schemes. Most commonly, these networks are represented as attributed graphs, with numerical features c…
University of Birjand（比尔詹德大学）；University of New Brunswick（新不伦瑞克大学）
Fraud detection is an important topic that applies to various enterprises such as banking and financial sectors, insurance, government agencies, law enforcement, and more. Fraud attempts have been risen remarkably in current years, shaping fraud detection an essential topic for research. One of the main challenges in fraud detection is acquiring fraud samples which is a complex and challenging task. In order to deal with this challenge, we apply one-class classification methods such as SVDD which does not need the fraud samples for training. Also, we present our algorithm REDBSCAN which is an …
A Hamiltonian driven Geometric Construction of Neural Networks via the Lognormal family, Application to Financial Fraud Detection and to Network Security
University of Yaounde1（雅温迪大学）；University of Maroua（马拉乌大学）；Higher national school of Polytechnic（国家高等理工学院）
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…
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）
The insurance industry has been creating innovative products around the emerging online shopping activities. Such e-commerce insurance is designed to protect buyers from potential risks such as impulse purchases and counterfeits. Fraudulent claims towards online insurance typically involve multiple parties such as buyers, sellers, and express companies, and they could lead to heavy financial losses. In order to uncover the relations behind organized fraudsters and detect fraudulent claims, we developed a large-scale insurance fraud detection system, i.e., InfDetect, which provides interfaces f…
Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE（纽约大学阿布扎比分校（NYUAD）工程学院 eBRAIN 实验室，阿布扎赫，阿联酋）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, UAE（量子与拓扑系统中心（CQTS），NYUAD 研究院，纽约大学阿布扎比分校（NYUAD），阿布扎赫，阿联酋）
QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction
Department of Physics, Indian Institute of Science Education and Research（印度科学教育与研究学院物理系）；Bikash’s Quantum (OPC) Pvt. Ltd.（比克ash量子（OPC）私人有限公司）；Nottingham Trent University（诺丁汉特伦特大学）；Qatar Center for Quantum Computing, College of Science and Engineering（卡塔尔量子计算中心，科学与工程学院）；Hamad Bin Khalifa University（哈马德·本·卡西姆大学）；Department of Computer Science, Faculty of Computers and Artificial Intelligence（计算机科学系，计算机与人工智能学院）
Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection
organization= Department of Computer Science \& Engineering, Khulna University of Engineering \& Technology (KUET) , city= Khulna , postcode= 9203 , country= Bangladesh；organization= Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= USA；organization= Department of Computer Science, American International University-Bangladesh (AIUB) , city= Dhaka , postcode= 1229 , country= Bangladesh
