Financial Fraud Detection

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Overview

Financial fraud detection studies how to identify deceptive activity in payments, banking, insurance, markets, and digital assets, often when fraud is rare and its patterns change over time. Common approaches include gradient-boosted models and anomaly detection on transaction features, graph neural networks for relationships among accounts and transactions, and temporal or sequence models; synthetic data, federated learning, and language models are also used. Methods are evaluated on benchmarks such as credit-card transaction datasets, cryptocurrency graphs, and simulated anti-money-laundering networks, with attention to precision-recall and the number of alerts investigators can handle. A central challenge is whether reported gains hold under chronological, leakage-safe evaluation and real operational constraints, where delayed or incomplete labels, distribution shift, privacy, and false alarms all affect deployment.

Latest papers

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137 papers20 papers in the last four weeks, up 33% on the four weeks before. 0.1% of all new papers.

  1. Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling

    Mar 14, 2026Dingding Cao, Bianbian Jiao, Jingzong Yang +2Financial Fraud DetectionDecentralized Finance

    School of Big Data, Baoshan University, Baoshan 678000, Yunnan, China · School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, Shandong, China

  2. Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE

    Mar 13, 2026Holger R. Roth, Sarthak Tickoo, Mayank Kumar +18Financial Fraud DetectionAnomaly Detection

    NVIDIA, Santa Clara, USA · J.P. Morgan, New York, USA · DeepTempo, San Francisco, USA +2

  3. EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection

    Mar 13, 2026En-Ya Kuo, Sebastien MotschSynthetic Data GenerationSynthetic Tabular Data Generation

    Arizona State University – School of Mathematical and Statistical Sciences, Tempe, AZ, USA

  4. FraudFox: Adaptable Fraud Detection in the Real World

    Mar 13, 2026Matthew Butler, Yi Fan, Christos FaloutsosFinancial Fraud DetectionEnsemble Learning

    Amazon.com, Inc., 410 Terry Ave N, Seattle WA 98109, U.S.A. · Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, U.S.A.

  5. Multilingual Financial Fraud Detection Using Machine Learning and Transformer Models: A Bangla-English Study

    Mar 11, 2026Mohammad Shihab Uddin, Md Hasibul Amin, Nusrat Jahan Ema +3Financial Fraud DetectionText Classification

    Augusta University, Augusta, GA 30912, USA · University of Houston, Houston, TX 77004, USA

  6. Algorithmic Compliance and Regulatory Loss in Digital Assets

    Mar 4, 2026Khem Raj Bhatt, Krishna SharmaAnti-Money LaunderingAnti-Money Laundering Detection

  7. Multi-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection

    Mar 3, 2026Jiaqi Lv, Qingfeng Du, Yu Zhang +2Graph TransformerAnomaly Detection

    School of Computer Science and Technology, Tongji University, Shanghai, China

  8. ERP-RiskBench: Leakage-Safe Ensemble Learning for Financial Risk

    Mar 3, 2026Sanjay MishraStacked Ensemble LearningFinancial Fraud Detection

    IEEE Member Raleigh, NC, USA

  9. Tide: A Customisable Dataset Generator for Anti-Money Laundering Research

    Mar 2, 2026Montijn van den Beukel, Jože Martin Rožanec, Ana-Lucia VarbanescuAnti-Money Laundering DetectionSynthetic Data Generation

    University of Amsterdam, The Netherlands · Jožef Stefan Institute, Slovenia · University of Amsterdam/University of Twente, The Netherlands

  10. TAS-GNN: A Status-Aware Signed Graph Neural Network for Anomaly Detection in Bitcoin Trust Systems

    Feb 28, 2026Chang Xue, Fang Liu, Jiaye Wang +2Anomaly DetectionGraph Neural Networks

    Yeshiva University · Yale University · University of California, San Diego +2

  11. Normalisation and Initialisation Strategies for Graph Neural Networks in Blockchain Anomaly Detection

    Feb 27, 2026Dang Sy Duy, Nguyen Duy Chien, Kapil Dev +1Graph Neural NetworksNeural Network Initialization

    RMIT University, Hanoi, Vietnam · Vietnam National University, Hanoi, Vietnam

  12. Leakage Safe Graph Features for Interpretable Fraud Detection in Temporal Transaction Networks

    Feb 24, 2026Hamideh Khaleghpour, Brett McKinneyFinancial Fraud DetectionTemporal Data Leakage

    The University of Tulsa, Tulsa, Oklahoma, USA

  13. HybridFL: A Federated Learning Approach for Financial Crime Detection

    Feb 22, 2026Afsana Khan, Marijn ten Thij, Guangzhi Tang +1Anti-Money LaunderingFederated Learning

    Department of Advanced Computing Sciences, Maastricht University, Netherlands · Department of Cognitive Science and Artificial Intelligence, Tilburg University, Netherlands

  14. A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems

    Feb 17, 2026Nasim Abdirahman Ismail, Enis KaraarslanFinancial Fraud DetectionGenerative Adversarial Networks

    Mugla Sitki Kocman University · Department of Computer Engineering, Mugla Sitki Kocman University

  15. Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud

    Jan 16, 2026Rongkun Cui, Nana Zhang, Kun Zhu +1Long-Tail LearningMulti-View Learning

    School of Computer Science and Technology, Tongji University Shanghai, China · School of Computer Science and Technology, Donghua University Shanghai, China

  16. Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling

    Jul 19, 2025Claudio Giusti, Luca Guarnera, Mirko Casu +1Class-Imbalanced LearningSynthetic Data Augmentation

    Department of Mathematics and Computer Science University of Catania Catania, CT 95125

  17. GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering

    Jun 4, 2025Bruno Deprez, Bart Baesens, Tim Verdonck +1Anti-Money LaunderingAnti-Money Laundering Detection

    KU Leuven · University of Antwerp - imec · University of Southampton