The Repository of Awesome Graph-based Financial Fraud Detection Papers and Codes. This repository is the official code of our survey paper:
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Graph Neural Networks for Financial Fraud Detection: A Review (Frontiers of Computer Science 2024) [Paper] [PDF] [Cite]
Dawei Cheng, Yao Zou, Sheng Xiang, Changjun Jiang
In our literature review, we collect, classify, and discuss recent graph-based fraud detection papers. Below is the detailed classification and paper with code (if available).
This is a curated list of research papers focusing on financial fraud detection using Graph Neural Networks (GNNs) from various conferences and Journals:
- Artificial Intelligence
- Data Science
- Network Science
This list aims to provide a comprehensive overview of research papers that utilize Graph Neural Networks for financial fraud detection across various academic conferences and disciplines.
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Parallel Graph Learning with Temporal Stamp Encoding for Fraudulent Transactions Detections (IEEE T-BD) Paper
Jiacheng Ma, Sheng Xiang, Qiang Li, Liangyu Yuan, Dawei Cheng, Changjun Jiang
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Subgraph Patterns Enhanced Graph Neural Network for Fraud Detection (DASFAA) [Paper]
Yao Zou, Sheng Xiang, Qijun Miao, Dawei Cheng, Changjun Jiang
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Effective High-order Graph Representation Learning for Credit Card Fraud Detection (IJCAI) [Paper] [Code]
Yao Zou, Dawei Cheng
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Safeguarding Fraud Detection from Attacks: A Robust Graph Learning Approach (IJCAI) [Paper]
Jiasheng Wu, Xin Liu, Dawei Cheng, Yi Ouyang, Xian Wu, Yefeng Zheng
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Pre-trained Online Contrastive Learning for Insurance Fraud Detection (AAAI) [Paper] [Code]
Rui Zhang, Dawei Cheng, Jie Yang, Yi Ouyang, Xian Wu, Yefeng Zheng, Changjun Jiang
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Partitioning Message Passing for Graph Fraud Detection (ICLR) [Paper] [Code]
Wei Zhuo , Zemin Liu , Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony , Jia Chen
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Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision (ICLR) [Paper] [Code]
Nan Chen , Zemin Liu , Bryan Hooi , Bingsheng He , Rizal Fathony , Jun Hu , Jia Chen
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Boosting Graph Anomaly Detection with Adaptive Message Passing (ICLR) [Paper] [Code]
Jingyan Chen, Guanghui Zhu , Chunfeng Yuan, Yihua Huang
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DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection (AAAI) [Paper] [Code]
Mingjiang Duan, Tongya Zheng, Yang Gao , Gang Wang, Zunlei Feng, Xinyu Wang
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DiG-In-GNN: Discriminative Feature Guided GNN-Based Fraud Detector against Inconsistencies in Multi-Relation Fraud Graph (AAAI) [Paper] [Code]
Jinghui Zhang, Zhengjia Xu, Dingyang Lv, Zhan Shi, Dian Shen, Jiahui Jin, Fang Dong
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Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation (AAAI) [Paper] [Code]
Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li, Ruihui Zhao, Yi Ouyang, Ling Chen, Yefeng Zheng
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FIW-GNN: A Heterogeneous Graph-Based Learning Model for Credit Card Fraud Detection (DSAA) [Paper]
Yan, Kuan, Gao, Junbin, Matsypura, Dmytro
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Unsupervised Fraud Transaction Detection on Dynamic Attributed Networks (DSAA) [Paper]
Yangyang Hou, Daixin Wang, Binbin Hu, Ruoyu Zhuang, Zhiqiang Zhang, Jun Zhou, Feng Zhao, Yulin Kang, Zhanwen Qiao
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Dynamic graph neural network-based fraud detectors against collaborative fraudsters (KBS) [Paper]
Lingfei Ren, Ruimin Hu, Dengshi Li, Yang Liu, Junhang Wu, Yilong Zang, Wenyi Hu
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Anti-Money Laundering by Group-Aware Deep Graph Learning (TKDE) [Paper]
Dawei Cheng, Yujia Ye, Sheng Xiang, Zhenwei Ma, Ying Zhang, Changjun Jiang
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Realistic Synthetic Financial Transactions for Anti-Money Laundering Models (NeurIPS) [Paper]
Erik Altman, Jovan Blanuša, Luc von Niederhäusern, Beni Egressy, Andreea Anghel, Kubilay Atasu
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MIDLG: Mutual Information based Dual Level GNN for Transaction Fraud Complaint Verification (KDD) [Paper]
Wen Zheng, Bingbing Xu, Emiao Lu, Yang Li, Qi Cao, Xuan Zong, and Huawei Shen
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Internet Financial Fraud Detection Based on Graph Learning (IEEE T-CSS) [Paper]
Ranran Li , Zhaowei Liu , Yuanqing Ma, Dong Yang, Shuaijie Sun
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Removing Camouflage and Revealing Collusion: Leveraging Gang-crime Pattern in Fraudster Detection (KDD-ADS) [Paper] [Code]
L Wang, H Zhao, C Feng, W Liu, C Huang, M Santoni, M Cristofaro, P Jafrancesco, J Bian
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Diga: Guided Diffusion Model for Graph Recovery in Anti-Money Laundering (KDD-ADS) [Paper]
X Li, Y Li, X Mo, H Xiao, Y Shen, L Chen
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Label Information Enhanced Fraud Detection against Low Homophily in Graphs (WWW) [Paper] [Code]
Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, Beilun Wang, Junzhou Luo
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Explainable Graph-based Fraud Detection via Neural Meta-graph Search (CIKM) [Paper]
Zidi Qin, Yang Liu, Qing He, Xiang Ao
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The Importance of Future Information in Credit Card Fraud Detection (AISTATS) [Paper]
Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini
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Inductive Graph Representation Learning for fraud detection (ESWA) [Paper]
Rafaël Van Belle, Charles Van Damme, Hendrik Tytgat, Jochen De Weerdt
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Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin (ArXiv) [Paper]
Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan, Siamak Layeghy, Marius Portmann
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BRIGHT - Graph Neural Networks in Real-time Fraud Detection (CIKM) [Paper]
Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao, Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang
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Ethereum Fraud Detection with Heterogeneous Graph Neural Networks (KDD) [Paper]
Hiroki Kanezashi, Toyotaro Suzumura, Xin Liu, Takahiro Hirofuchi
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xFraud: Explainable Fraud Transaction Detection (ArXiv) [Paper]
Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Zhiyao Chen, Yinan Shan, Yang Zhao, Ce Zhang
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ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection (TCSS) [Paper]
Yue Tian, Guanjun Liu, Jiacun Wang, Mengchu Zhou
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Rethinking Graph Neural Networks for Anomaly Detection (ICML) [Paper] [Code]
Tang, Jianheng and Li, Jiajin and Gao, Ziqi and Li, Jia
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H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections (WWW) [Paper] [Code]
Shi, Fengzhao and Cao, Yanan and Shang, Yanmin and Zhou, Yuchen and Zhou, Chuan and Wu, Jia
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CaT-GNN: Enhancing Credit Card Fraud Detection with Causal Time Graph Neural Networks (TKDE) [Paper]
Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang
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Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field (AAAI) [Paper]
Bingbing Xu, Huawei Shen, Bing-Jie Sun, Rong An, Qi Cao, Xueqi Cheng
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Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection (AAAI) [Paper]
Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He
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Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks (AAAI) [Paper]
Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Mo Cheng, Yinan Shan, Yang Zhao, Ce Zhang
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Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (WWW) [Paper] [Code]
Liu, Yang and Ao, Xiang and Qin, Zidi and Chi, Jianfeng and Feng, Jinghua and Yang, Hao and He, Qing
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FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance (ICDM) [Paper] [Code]
Zhang, Ge and Wu, Jia and Yang, Jian and Beheshti, Amin and Xue, Shan and Zhou, Chuan and Sheng, Quan Z
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Anomaly Detection in Dynamic Graphs via Transformer (TKDE) [Paper]
Y Liu, S Pan, YG Wang, F Xiong, L Wang, Q Chen, VCS Lee
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Graph Neural Network for Fraud Detection via Spatial-Temporal Attention (TKDE) [Paper] [Code]
Dawei Cheng, Xiaoyang Wang, Ying Zhang, Liqing Zhang
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Parallel granular neural networks for fast credit card fraud detection (APIN) [Paper]
Syeda, M, Yan-Qing Zhang, Yi Pan
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FlowScope: Spotting Money Laundering Based on Graphs (AAAI) [Paper] [Code]
Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng
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Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain (ICML) [Paper]
Ismail Alarab, Simant Prakoonwit, Mohamed Ikbal Nacer
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Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (CIKM) [Paper] [Code]
Dou, Yingtong and Liu, Zhiwei and Sun, Li and Deng, Yutong and Peng, Hao and Yu, Philip S.
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FlowScope: Spotting Money Laundering Based on Graphs (AAAI) [Paper] [Code]
Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi , Bryan Hooi, He Huang, Xueqi Cheng
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Uncovering insurance fraud conspiracy with network learning (SIGIR) [Paper]
Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, and Yuan Qi
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Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics (SIGKDD) [Paper]
Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, Charles E. Leiserson
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Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism (AAAI) [Paper]
Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi
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Auto-encoder based Graph Convolutional Networks for Online Financial Anti-fraud (ICEFr) [Paper]
Lv, Le and Cheng, Jianbo and Peng, Nanbo and Fan, Min and Zhao, Dongbin, Zhang, Jianhong
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Scalable Graph Learning for Anti-Money Laundering: A First Look (ArXiv) [Paper]
Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl
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Heterogeneous Graph Neural Networks for Malicious Account Detection (CIKM) [Paper]
Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song
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Graph Mining assisted Semi-supervised Learning for Fraudulent Cash-out Detection (KDD) [Paper]
Yuan Li, Yiheng Sun, and Noshir Contractor
For related collections on graph-based methods in other domains, please refer to:
- Graph Classification
- Classification/Regression Tree
- Gradient Boosting
- Monte Carlo Tree Search
- Community Detection
If you find this literature review is useful for your research, please consider citing the following papers:
@article{cheng2024graph,
title={Graph Neural Networks for Financial Fraud Detection: A Review},
author={Cheng, Dawei and Zou, Yao and Xiang, Sheng and Jiang, Changjun},
journal={arXiv preprint arXiv:2411.05815},
year={2024}
}