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 over-linkages by entities that could be widely shared (ie. ip address), making it more difficult to learn useful information from graph. To address aforementioned problems, we present a novel behavioral biometric based method to establish transaction linkings based on user behavioral similarities, then train an unsupervised GNN to extract embedding features for downstream fraud prediction tasks. To our knowledge, this is the first time similarity based soft link has been used in graph embedding applications. To speed up similarity calculation, we apply an in-house GPU based HDBSCAN clustering method to remove highly concentrated and isolated nodes before graph construction. Our experiments show that embedding features learned from similarity based behavioral graph have achieved significant performance increase to the baseline fraud detection model in various business scenarios. In new guest buyer transaction scenario, this segment is a challenge for traditional method, we can make precision increase from 0.82 to 0.86 at the same recall of 0.27, which means we can decrease false positive rate using this method.
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院智能信息处理重点实验室，中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Alipay (Hangzhou) Information & Technology Co., Ltd.（支付宝（杭州）信息技术有限公司）
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）
Transaction Fraud Detection Using GRU-centered Sandwich-structured Model
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）
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision
Zhejiang University of Technology（浙江工业大学）；Binjiang Institute of Artificial Intelligence, ZJUT（浙江工业大学滨江人工智能研究院）；Science and Technology on Communication Information Security Control Laboratory（通信信息安全控制技术实验室）
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）
CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
School of Information and Communication Technology, Griffith University, Australia（格里菲斯大学信息与通信技术学院，澳大利亚）；Department of Computer Science and Information Technology, La Trobe University, Australia（拉特罗布大学计算机科学与信息技术系，澳大利亚）
A Machine Learning Driven Website Platform and Browser Extension for Real-time Scoring and Fraud Detection for Website Legitimacy Verification and Consumer Protection
University of Central Arkansas（中阿肯色大学）；University of Texas at El Paso（德克萨斯大学埃尔帕索分校）
Dual-channel Heterophilic Message Passing for Graph Fraud Detection
University of Chinese Academy of Science（中国科学院大学）；Fuzhou University（福州大学）；Northeast Normal University（东北师范大学）；Hainan University（海南大学）；Tsinghua University（清华大学）；The Australian National University（澳大利亚国立大学）
Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection
Tongji University Shanghai China；The University of New South Wales Sydney Australia；Tongji University \& Shanghai Artificial Intelligence Laboratory Shanghai China；Wechat Pay, Tencent Inc. Shenzhen China；Tongji University；The University of New South Wales；Tongji University \& Shanghai Artificial Intelligence Laboratory；Wechat Pay, Tencent Inc.
Non-Intrusive Graph-Based Bot Detection for E-Commerce Using Inductive Graph Neural Networks
College of Engineering Northeastern University Boston, USA（工程学院 哈佛大学 波士顿 美国）；Khoury College of Computer Sciences Northeastern University Boston, USA（计算机科学学院 哈佛大学 波士顿 美国）；College of Professional Studies Northeastern University Boston, USA（专业研究学院 哈佛大学 波士顿 美国）
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection
National University of Singapore（新加坡国立大学）；Shopee Singapore（Shopee新加坡）；Zhejiang University（浙江大学）；University of Michigan（密歇根大学）；Beijing Institute of Technology（北京理工大学）
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）
