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Below is the detailed classification and paper with code (if available). 13 | 14 | ## Conference & Journal Article 15 | This is a curated list of research papers focusing on financial fraud detection using Graph Neural Networks (GNNs) from various conferences and Journals: 16 | - **Artificial Intelligence** 17 | - [NeurIPS](https://nips.cc/) 18 | - [ICML](https://icml.cc/) 19 | - [ICLR](https://iclr.cc/) 20 | - [AAAI](https://www.aaai.org/) 21 | - [IJCAI](https://www.ijcai.org/) 22 | - [AISTATS](http://www.auai.org/) 23 | - [UAI](http://www.auai.org/) 24 | - **Data Science** 25 | - [KDD](https://www.kdd.org/) 26 | - [TKDE](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69) 27 | - [WWW](https://www2019.thewebconf.org/) 28 | - [SIGIR](https://sigir.org/) 29 | - [ICDM](http://icdm2019.bigke.org/) 30 | - [SDM](https://www.siam.org/conferences/cm/conference/sdm20) 31 | - [WSDM](https://www.wsdm-conference.org/) 32 | - [CIKM](http://www.cikmconference.org/) 33 | - **Network Science** 34 | - [ASONAM](http://asonam.cpsc.ucalgary.ca/2019/) 35 | - [COMPLEX NETWORKS](https://www.complexnetworks.org/) 36 | - [NetSci](https://netscisociety.net/) 37 | 38 | 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. 39 | 40 | ### 2024 41 | - **Parallel Graph Learning with Temporal Stamp Encoding for Fraudulent Transactions Detections (IEEE T-BD)** [Paper](https://ieeexplore.ieee.org/abstract/document/10753618/) 42 | 43 | *Jiacheng Ma, Sheng Xiang, Qiang Li, Liangyu Yuan, Dawei Cheng, Changjun Jiang* 44 | 45 | 46 | - **Subgraph Patterns Enhanced Graph Neural Network for Fraud Detection (DASFAA)** [[Paper]](https://link.springer.com/chapter/10.1007/978-981-97-5572-1_26) 47 | 48 | *Yao Zou, Sheng Xiang, Qijun Miao, Dawei Cheng, Changjun Jiang* 49 | 50 | 51 | - **Effective High-order Graph Representation Learning for Credit Card Fraud Detection (IJCAI)** [[Paper]](https://arxiv.org/pdf/2503.01556) [[Code]](https://github.com/finint/antifraud) 52 | 53 | *Yao Zou, Dawei Cheng* 54 | 55 | 56 | - **Safeguarding Fraud Detection from Attacks: A Robust Graph Learning Approach (IJCAI)** [[Paper]](https://www.ijcai.org/proceedings/2024/0830.pdf) 57 | 58 | *Jiasheng Wu, Xin Liu, Dawei Cheng, Yi Ouyang, Xian Wu, Yefeng Zheng* 59 | 60 | 61 | - **Pre-trained Online Contrastive Learning for Insurance Fraud Detection (AAAI)** [[Paper]](https://doi.org/10.1609/aaai.v38i20.30259) [[Code]](https://github.com/AI4Risk/POCL) 62 | 63 | *Rui Zhang, Dawei Cheng, Jie Yang, Yi Ouyang, Xian Wu, Yefeng Zheng, Changjun Jiang* 64 | 65 | ![](https://img.shields.io/badge/Online%20Learning-A52A2A) ![](https://img.shields.io/badge/Contrastive%20Learning%20Pre--training-A52A2A) 66 | 67 | - **Partitioning Message Passing for Graph Fraud Detection (ICLR)** [[Paper]](https://openreview.net/pdf?id=tEgrUrUuwA) [[Code]](https://github.com/Xtra-Computing/PMP) 68 | 69 | *Wei Zhuo , Zemin Liu , Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony , Jia Chen* 70 | 71 | - **Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision (ICLR)** [[Paper]](https://openreview.net/pdf?id=elMKXvhhQ9) [[Code]](https://github.com/Xtra-Computing/ConsisGAD) 72 | 73 | *Nan Chen , Zemin Liu , Bryan Hooi , Bingsheng He , Rizal Fathony , Jun Hu , Jia Chen* 74 | 75 | - **Boosting Graph Anomaly Detection with Adaptive Message Passing (ICLR)** [[Paper]](https://openreview.net/pdf?id=CanomFZssu) [[Code]](https://github.com/PasaLab/GADAM) 76 | 77 | *Jingyan Chen, Guanghui Zhu , Chunfeng Yuan, Yihua Huang* 78 | 79 | - **DGA-GNN: Dynamic Grouping Aggregation GNN for Fraud Detection (AAAI)** [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/29067) [[Code]](https://github.com/AtwoodDuan/DGA-GNN) 80 | 81 | *Mingjiang Duan, Tongya Zheng, Yang Gao , Gang Wang, Zunlei Feng, Xinyu Wang* 82 | 83 | - **DiG-In-GNN: Discriminative Feature Guided GNN-Based Fraud Detector against Inconsistencies in Multi-Relation Fraud Graph (AAAI)** [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/28785) [[Code]](https://github.com/GraphBerry/DiG-In-GNN) 84 | 85 | *Jinghui Zhang, Zhengjia Xu, Dingyang Lv, Zhan Shi, Dian Shen, Jiahui Jin, Fang Dong* 86 | 87 | ### 2023 88 | 89 | - **Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation (AAAI)** [[Paper]](https://www.xiangshengcloud.top/publication/semi-supervised-credit-card-fraud-detection-via-attribute-driven-graph-representation/Sheng-AAAI2023.pdf) [[Code]](https://github.com/finint/antifraud) 90 | 91 | *Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li, Ruihui Zhao, Yi Ouyang, Ling Chen, Yefeng Zheng* 92 | 93 | ![](https://img.shields.io/badge/Semi--Supervised%20Learning-A52A2A) ![](https://img.shields.io/badge/Risk%20Propagation-A52A2A) 94 | 95 | - **FIW-GNN: A Heterogeneous Graph-Based Learning Model for Credit Card Fraud Detection (DSAA)** [[Paper]](https://ieeexplore.ieee.org/abstract/document/10302538) 96 | 97 | *Yan, Kuan, Gao, Junbin, Matsypura, Dmytro* 98 | 99 | - **Unsupervised Fraud Transaction Detection on Dynamic Attributed Networks (DSAA)** [[Paper]](https://link.springer.com/chapter/10.1007/978-3-031-30678-5_41) 100 | 101 | *Yangyang Hou, Daixin Wang, Binbin Hu, Ruoyu Zhuang, Zhiqiang Zhang, Jun Zhou, Feng Zhao, Yulin Kang, Zhanwen Qiao* 102 | 103 | - **Dynamic graph neural network-based fraud detectors against collaborative fraudsters (KBS)** [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S095070512300638X) 104 | 105 | *Lingfei Ren, Ruimin Hu, Dengshi Li, Yang Liu, Junhang Wu, Yilong Zang, Wenyi Hu* 106 | 107 | - **Anti-Money Laundering by Group-Aware Deep Graph Learning (TKDE)** [[Paper]](https://doi.org/10.1109/TKDE.2023.3272396) 108 | 109 | *Dawei Cheng, Yujia Ye, Sheng Xiang, Zhenwei Ma, Ying Zhang, Changjun Jiang* 110 | 111 | - **Realistic Synthetic Financial Transactions for Anti-Money Laundering Models (NeurIPS)** [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2023/hash/5f38404edff6f3f642d6fa5892479c42-Abstract-Datasets_and_Benchmarks.html) 112 | 113 | *Erik Altman, Jovan Blanuša, Luc von Niederhäusern, Beni Egressy, Andreea Anghel, Kubilay Atasu* 114 | 115 | - **MIDLG: Mutual Information based Dual Level GNN for Transaction Fraud Complaint Verification (KDD)** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3580305.3599865) 116 | 117 | *Wen Zheng, Bingbing Xu, Emiao Lu, Yang Li, Qi Cao, Xuan Zong, and Huawei Shen* 118 | 119 | - **Internet Financial Fraud Detection Based on Graph Learning (IEEE T-CSS)** [[Paper]](https://ieeexplore.ieee.org/abstract/document/9831118) 120 | 121 | *Ranran Li , Zhaowei Liu , Yuanqing Ma, Dong Yang, Shuaijie Sun* 122 | 123 | - **Removing Camouflage and Revealing Collusion: Leveraging Gang-crime Pattern in Fraudster Detection (KDD-ADS)** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3580305.3599895) [[Code]](https://github.com/lwwang1995/ACD) 124 | 125 | *L Wang, H Zhao, C Feng, W Liu, C Huang, M Santoni, M Cristofaro, P Jafrancesco, J Bian* 126 | 127 | - **Diga: Guided Diffusion Model for Graph Recovery in Anti-Money Laundering (KDD-ADS)** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3580305.3599806) 128 | 129 | *X Li, Y Li, X Mo, H Xiao, Y Shen, L Chen* 130 | 131 | - **Label Information Enhanced Fraud Detection against Low Homophily in Graphs (WWW)** [[Paper]](https://arxiv.org/pdf/2302.10407) [[Code]](https://github.com/hoangdzung/GAGA/tree/master) 132 | 133 | *Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, Beilun Wang, Junzhou Luo* 134 | 135 | 136 | ### 2022 137 | - **Explainable Graph-based Fraud Detection via Neural Meta-graph Search (CIKM)** [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3511808.3557598) 138 | 139 | *Zidi Qin, Yang Liu, Qing He, Xiang Ao* 140 | 141 | - **The Importance of Future Information in Credit Card Fraud Detection (AISTATS)** [[Paper]](https://arxiv.org/abs/2204.05265) 142 | 143 | *Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini* 144 | 145 | - **Inductive Graph Representation Learning for fraud detection (ESWA)** [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S0957417421017449) 146 | 147 | *Rafaël Van Belle, Charles Van Damme, Hendrik Tytgat, Jochen De Weerdt* 148 | 149 | - **Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin (ArXiv)** [[Paper]](https://doi.org/10.1007/s10489-023-04504-9) 150 | 151 | *Wai Weng Lo, Gayan K. Kulatilleke, Mohanad Sarhan, Siamak Layeghy, Marius Portmann* 152 | 153 | - **BRIGHT - Graph Neural Networks in Real-time Fraud Detection (CIKM)** [[Paper]](https://arxiv.org/abs/2205.13084) 154 | 155 | *Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao, Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang* 156 | 157 | - **Ethereum Fraud Detection with Heterogeneous Graph Neural Networks (KDD)** [[Paper]](https://arxiv.org/abs/2203.12363) 158 | 159 | *Hiroki Kanezashi, Toyotaro Suzumura, Xin Liu, Takahiro Hirofuchi* 160 | 161 | - **xFraud: Explainable Fraud Transaction Detection (ArXiv)** [[Paper]](https://arxiv.org/abs/2011.12193) 162 | 163 | *Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Zhiyao Chen, Yinan Shan, Yang Zhao, Ce Zhang* 164 | - **ASA-GNN: Adaptive Sampling and Aggregation-Based Graph Neural Network for Transaction Fraud Detection (TCSS)** [[Paper]](https://ieeexplore.ieee.org/abstract/document/10354439) 165 | 166 | *Yue Tian, Guanjun Liu, Jiacun Wang, Mengchu Zhou* 167 | 168 | - **Rethinking Graph Neural Networks for Anomaly Detection (ICML)** [[Paper]](https://proceedings.mlr.press/v162/tang22b.html) [[Code]](https://github.com/squareRoot3/Rethinking-Anomaly-Detection) 169 | 170 | *Tang, Jianheng and Li, Jiajin and Gao, Ziqi and Li, Jia* 171 | 172 | ![](https://img.shields.io/badge/Spectral%20Filter-A52A2A) ![](https://img.shields.io/badge/Right--Shift%20Phenomenon-A52A2A) 173 | 174 | - **H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections (WWW)** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3485447.3512195) [[Code]](https://github.com/shifengzhao/H2-FDetector) 175 | 176 | *Shi, Fengzhao and Cao, Yanan and Shang, Yanmin and Zhou, Yuchen and Zhou, Chuan and Wu, Jia* 177 | 178 | ![](https://img.shields.io/badge/Heterogeneity%20Detection-A52A2A) ![](https://img.shields.io/badge/Homogeneity%20Detection-A52A2A) 179 | 180 | ### 2021 181 | 182 | - **CaT-GNN: Enhancing Credit Card Fraud Detection with Causal Time Graph Neural Networks (TKDE)** [[Paper]](https://arxiv.dosf.top/abs/2402.14708) 183 | 184 | *Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang* 185 | 186 | - **Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field (AAAI)** [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16582) 187 | 188 | *Bingbing Xu, Huawei Shen, Bing-Jie Sun, Rong An, Qi Cao, Xueqi Cheng* 189 | 190 | - **Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection (AAAI)** [[Paper]](https://arxiv.org/abs/2008.05600) 191 | 192 | *Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He* 193 | 194 | - **Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks (AAAI)** [[Paper]](https://arxiv.org/abs/2012.10831) 195 | 196 | *Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Mo Cheng, Yinan Shan, Yang Zhao, Ce Zhang* 197 | 198 | - **Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (WWW)** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3442381.3449989) [[Code]](https://github.com/PonderLY/PC-GNN) 199 | 200 | *Liu, Yang and Ao, Xiang and Qin, Zidi and Chi, Jianfeng and Feng, Jinghua and Yang, Hao and He, Qing* 201 | 202 | ![](https://img.shields.io/badge/Imbalanced%20Learning-A52A2A) 203 | 204 | - **FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance (ICDM)** [[Paper]](https://ieeexplore.ieee.org/abstract/document/9679178) [[Code]](https://github.com/FraudDetection/FRAUDRE) 205 | 206 | *Zhang, Ge and Wu, Jia and Yang, Jian and Beheshti, Amin and Xue, Shan and Zhou, Chuan and Sheng, Quan Z* 207 | 208 | ![](https://img.shields.io/badge/Camouflage%20Detection-A52A2A) ![](https://img.shields.io/badge/Imbalanced%20Learning-A52A2A) 209 | 210 | - **Anomaly Detection in Dynamic Graphs via Transformer (TKDE)** [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9599560) 211 | 212 | *Y Liu, S Pan, YG Wang, F Xiong, L Wang, Q Chen, VCS Lee* 213 | 214 | ### 2020 215 | 216 | - **Graph Neural Network for Fraud Detection via Spatial-Temporal Attention (TKDE)** [[Paper]](https://ieeexplore.ieee.org/abstract/document/9204584) [[Code]](https://github.com/finint/antifraud) 217 | 218 | *Dawei Cheng, Xiaoyang Wang, Ying Zhang, Liqing Zhang* 219 | 220 | ![](https://img.shields.io/badge/Spatial--Temporal%20Attention-A52A2A) ![](https://img.shields.io/badge/3D%20Convolution-A52A2A) 221 | 222 | - **Parallel granular neural networks for fast credit card fraud detection (APIN)** [[Paper]](https://ieeexplore.ieee.org/abstract/document/1005055) 223 | 224 | *Syeda, M, Yan-Qing Zhang, Yi Pan* 225 | 226 | - **FlowScope: Spotting Money Laundering Based on Graphs (AAAI)** [[Paper] ](https://shenghua-liu.github.io/papers/aaai2020cr-flowscope.pdf)[[Code]](https://github.com/aplaceof/FlowScope) 227 | 228 | *Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng* 229 | 230 | - **Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain (ICML)** [[Paper]](https://www.semanticscholar.org/paper/Competence-of-Graph-Convolutional-Networks-for-in-Alarab-Prakoonwit/1225bc8902daa95f0df8d6a84b3cfdd4a1cca31d) 231 | 232 | *Ismail Alarab, Simant Prakoonwit, Mohamed Ikbal Nacer* 233 | 234 | - **Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (CIKM)** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3340531.3411903) [[Code]](https://github.com/YingtongDou/CARE-GNN) 235 | 236 | *Dou, Yingtong and Liu, Zhiwei and Sun, Li and Deng, Yutong and Peng, Hao and Yu, Philip S.* 237 | 238 | ![](https://img.shields.io/badge/Camouflage%20Detection-A52A2A) ![](https://img.shields.io/badge/Reinforcement%20Learning-A52A2A) 239 | 240 | - **FlowScope: Spotting Money Laundering Based on Graphs (AAAI)** [[Paper] ](https://aaai.org/ojs/index.php/AAAI/article/download/5906/5762) [[Code]](https://github.com/csqjxiao/FlowScope) 241 | 242 | *Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi , Bryan Hooi, He Huang, Xueqi Cheng* 243 | 244 | ### 2019 245 | 246 | - **Uncovering insurance fraud conspiracy with network learning (SIGIR)** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3331184.3331372) 247 | 248 | *Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, and Yuan Qi* 249 | 250 | - **Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics (SIGKDD)** [[Paper]](https://arxiv.org/abs/1908.02591) 251 | 252 | *Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, Charles E. Leiserson* 253 | 254 | - **Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism (AAAI)** [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/3884) 255 | 256 | *Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi* 257 | 258 | - **Auto-encoder based Graph Convolutional Networks for Online Financial Anti-fraud (ICEFr)** [[Paper]](https://ieeexplore.ieee.org/abstract/document/8759109) 259 | 260 | *Lv, Le and Cheng, Jianbo and Peng, Nanbo and Fan, Min and Zhao, Dongbin, Zhang, Jianhong* 261 | 262 | ### 2018 263 | 264 | - **Scalable Graph Learning for Anti-Money Laundering: A First Look (ArXiv)** [[Paper]](https://www.semanticscholar.org/paper/Scalable-Graph-Learning-for-Anti-Money-Laundering%3A-Weber-Chen/435a772dc696de13a185e33e80dd7fe71b25dcf0) 265 | 266 | *Mark Weber, Jie Chen, Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl* 267 | - **Heterogeneous Graph Neural Networks for Malicious Account Detection (CIKM)** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3269206.3272010) 268 | 269 | *Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song* 270 | 271 | ### 2017 272 | 273 | - **Graph Mining assisted Semi-supervised Learning for Fraudulent Cash-out Detection (KDD)** [[Paper]](https://dl.acm.org/doi/abs/10.1145/3110025.3110099) 274 | 275 | *Yuan Li, Yiheng Sun, and Noshir Contractor* 276 | 277 | ### Related Topics 278 | 279 | For related collections on graph-based methods in other domains, please refer to: 280 | 281 | - [Graph Classification](https://github.com/benedekrozemberczki/awesome-graph-classification) 282 | - [Classification/Regression Tree](https://github.com/benedekrozemberczki/awesome-decision-tree-papers) 283 | - [Gradient Boosting](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers) 284 | - [Monte Carlo Tree Search](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers) 285 | - [Community Detection](https://github.com/benedekrozemberczki/awesome-community-detection) 286 | 287 | ## Citing 288 | If you find this literature review is useful for your research, please consider citing the following papers: 289 | 290 | 291 | @article{cheng2024graph, 292 | title={Graph Neural Networks for Financial Fraud Detection: A Review}, 293 | author={Cheng, Dawei and Zou, Yao and Xiang, Sheng and Jiang, Changjun}, 294 | journal={arXiv preprint arXiv:2411.05815}, 295 | year={2024} 296 | } 297 | 298 | 299 | 300 | ## All Thanks to Our Contributors : 301 | 302 | 303 | 304 | 305 | 306 | --------------------------------------------------------------------------------