With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity fraud. Fraudsters may use these technologies to create highly sophisticated counterfeit personal identification documents, photos and videos. These advancements in the identity fraud landscape pose challenges for identity fraud detection and society at large. There is a pressing need to review and understand identity fraud detection methods, their limitations and potential solutions. This research aims to address this important need by using the well-known systematic literature review method. This paper reviewed a selected set of 43 papers across 4 major academic literature databases. In particular, the review results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. The results were also consolidated into a taxonomy of AI-based identity fraud detection and prevention methods including key insights and trends. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.
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）
Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user wh…
This work presents a fraud and abuse detection framework for streaming services by modeling user streaming behavior. The goal is to discover anomalous and suspicious incidents and scale the investigation efforts by creating models that characterize the user behavior. We study the use of semi-supervised as well as supervised approaches for anomaly detection. In the semi-supervised approach, by leveraging only a set of authenticated anomaly-free data samples, we show the use of one-class classification algorithms as well as autoencoder deep neural networks for anomaly detection. In the supervise…
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
Are You for Real? Detecting Identity Fraud via Dialogue Interactions
Institute of Automation, CAS（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Mobvoi（出门问问）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection
Arizona State University（亚利桑那州立大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Department of Homeland Security Science and Technology Directorate（国土安全部科学与技术局）
This paper evaluates XGboost's performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. XGBoost has been selected for evaluation, as it stands out in several benchmarks due to its detection performance and speed. After introducing the problem of fraud detection, the paper reviews evaluation metrics for detection systems or binary classifiers, and illustrates with examples how different metrics work for balanced and imbalanced datasets. Then, it examines the principles of XGBoost. It proposes a pipeline for data preparation and compares a …
Reinforcement Learning of Large Language Models for Interpretable Credit Card Fraud Detection
Hong Kong University of Science and Technology（香港科学与技术大学）；Hong Kong Baptist University（香港 Baptist 大学）；Imperial College London（伦敦帝国理工学院）；National University of Singapore（新加坡国立大学）
Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights
Google Deepmind（谷歌DeepMind）；Massachusetts Institute of Technology（麻省理工学院）；Independent Researcher（独立研究员）；Google（谷歌）；AI Accountability Lab, Trinity College Dublin（都柏林圣三一学院人工智能问责实验室）
