Awesome Fraud Detection Research Papers.
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A curated list of fraud detection papers from the following conferences:

- Network Science
   ⟡ ASONAM (http://asonam.cpsc.ucalgary.ca/2019/)
   ⟡ COMPLEX NETWORKS (https://www.complexnetworks.org/)
- Data Science
   ⟡ DSAA (http://dsaa2019.dsaa.co/)
- Natural Language Processing
   ⟡ ACL (http://www.acl2019.org/EN/index.xhtml)
- Data Mining
   ⟡ KDD (https://www.kdd.org/)
   ⟡ ICDM (http://icdm2019.bigke.org/)
   ⟡ SIGIR (https://sigir.org/)
   ⟡ SDM (https://www.siam.org/conferences/cm/conference/sdm20)
   ⟡ WWW (https://www2019.thewebconf.org/)
   ⟡ CIKM (http://www.cikmconference.org/)
- Artificial Intelligence
   ⟡ AAAI (https://www.aaai.org/)
   ⟡ AISTATS (http://www.auai.org/)
   ⟡ IJCAI (https://www.ijcai.org/)
   ⟡ UAI (http://www.auai.org/)
- Databases
   ⟡ VLDB (http://www.vldb.org/)

Similar collections about graph classification (https://github.com/benedekrozemberczki/awesome-graph-classification), classification/regression tree (https://github.com/benedekrozemberczki/awesome-decision-tree-papers), gradient boosting 
(https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers), Monte Carlo tree search (https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers), and community detection 
(https://github.com/benedekrozemberczki/awesome-community-detection) papers with implementations.


2023
- Anti-Money Laundering by Group-Aware Deep Graph Learning (TKDE 2023)
  - Dawei Cheng, Yujia Ye, Sheng Xiang, Zhenwei Ma, Ying Zhang, Changjun Jiang
  - Paper  (https://doi.org/10.1109/TKDE.2023.3272396)

- Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation (AAAI 2023)
  - Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li, Ruihui Zhao, Yi Ouyang, Ling Chen, Yefeng Zheng
  - 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)

- A Framework for Detecting Frauds from Extremely Few Labels (WSDM 2023)
  - Ya-Lin Zhang, Yi-Xuan Sun, Fangfang Fan, Meng Li, Yeyu Zhao, Wei Wang, Longfei Li, Jun Zhou, Jinghua Feng
  - Paper  (https://dl.acm.org/doi/10.1145/3539597.3573022)
 
- Label Information Enhanced Fraud Detection against Low Homophily in Graphs (WWW 2023)
  - Yuchen Wang, Jinghui Zhang, Zhengjie Huang, Weibin Li, Shikun Feng, Ziheng Ma, Yu Sun, Dianhai Yu, Fang Dong, Jiahui Jin, Beilun Wang, Junzhou Luo (WWW 2023)
  - Paper  (https://arxiv.org/abs/2302.10407)

- BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection (WWW 2023)
  - Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He, Ling Liu
  - Paper  (https://arxiv.org/abs/2303.18138)

2022

- The Importance of Future Information in Credit Card Fraud Detection (AISTATS 2022)
  - Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini
  - Paper  (https://arxiv.org/abs/2204.05265)

- BRIGHT - Graph Neural Networks in Real-time Fraud Detection (CIKM 2022)
  - Mingxuan Lu, Zhichao Han, Susie Xi Rao, Zitao Zhang, Yang Zhao, Yinan Shan, Ramesh Raghunathan, Ce Zhang, Jiawei Jiang
  - Paper  (https://arxiv.org/abs/2205.13084)

- Dual-Augment Graph Neural Network for Fraud Detection (CIKM 2022)
  - Qiutong Li, Yanshen He, Cong Xu, Feng Wu, Jianliang Gao, Zhao Li
  - Paper  (https://dl.acm.org/doi/10.1145/3511808.3557586)

- Explainable Graph-based Fraud Detection via Neural Meta-graph Search (CIKM 2022)
  - Zidi Qin, Yang Liu, Qing He, Xiang Ao
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3511808.3557598)

- MetaRule: A Meta-path Guided Ensemble Rule Set Learning for Explainable Fraud Detection (CIKM 2022)
  - Lu Yu, Meng Li, Xiaoguang Huang, Wei Zhu, Yanming Fang, Jun Zhou, Longfei Li
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3511808.3557641)

- User Behavior Pre-training for Online Fraud Detection (KDD 2022)
  - Can Liu, Yuncong Gao, Li Sun, Jinghua Feng, Hao Yang, Xiang Ao
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3534678.3539126)

- Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow (KDD 2022)
  - Brad Rees, Xiaoyun Wang, Joe Eaton, Onur Yilmaz, Rick Ratzel, Dominque LaSalle
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3534678.3542603)

- A View into YouTube View Fraud (WWW 2022)
  - Dhruv Kuchhal, Frank Li
  - Paper  (https://dl.acm.org/doi/10.1145/3485447.3512216)

- Beyond Bot Detection: Combating Fraudulent Online Survey Takers (WWW 2022)
  - Ziyi Zhang, Shuofei Zhu, Jaron Mink, Aiping Xiong, Linhai Song, Gang Wang
  - Paper  (https://gangw.cs.illinois.edu/www22-bot.pdf)

- AUC-oriented Graph Neural Network for Fraud Detection (WWW 2022)
  - Mengda Huang, Yang Liu, Xiang Ao, Kuan Li, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He
  - Paper  (https://ponderly.github.io/pub/AOGNN_WWW2022.pdf)

- H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections (WWW 2022)
  - Fengzhao Shi, Yanan Cao, Yanmin Shang, Yuchen Zhou, Chuan Zhou, Jia Wu
  - Paper  (https://dl.acm.org/doi/10.1145/3485447.3512195)

- Active Learning for Human-in-the-loop Customs Inspection (TKDE 2022)
  - Sundong Kim, Tung-Duong Mai, Thi Nguyen Duc Khanh, Sungwon Han, Sungwon Park, Karandeep Singh, Meeyoung Cha
  - Paper  (https://ieeexplore.ieee.org/document/9695316/)
  - Code  (https://github.com/Seondong/Customs-Fraud-Detection)

- Knowledge Sharing via Domain Adaptation in Customs Fraud Detection (AAAI 2022)
  - Sungwon Park, Sundong Kim, Meeyoung Cha
  - Paper  (https://arxiv.org/abs/2201.06759)

2021
- Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field (AAAI 2021)
  - Bingbing Xu, Huawei Shen, Bing-Jie Sun, Rong An, Qi Cao, Xueqi Cheng
  - Paper  (https://ojs.aaai.org/index.php/AAAI/article/view/16582)

- Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection (AAAI 2021)
  - Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He
  - Paper  (https://arxiv.org/abs/2008.05600)

- IFDDS: An Anti-fraud Outbound Robot (AAAI 2021)
  - Zihao Wang, Minghui Yang, Chunxiang Jin, Jia Liu, Zujie Wen, Saishuai Liu, Zhe Zhang
  - Paper  (https://ojs.aaai.org/index.php/AAAI/article/view/18030)

- Modeling Heterogeneous Graph Network on Fraud Detection: A Community-based Framework with Attention Mechanism (CIKM 2021)
  - Li Wang, Peipei Li, Kai Xiong, Jiashu Zhao, Rui Lin
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3459637.3482277)

- Fraud Detection under Multi-Sourced Extremely Noisy Annotations (CIKM 2021)
  - Chuang Zhang, Qizhou Wang, Tengfei Liu, Xun Lu, Jin Hong, Bo Han, Chen Gong
  - Paper  (https://gcatnjust.github.io/ChenGong/paper/zhang_cikm21.pdf)

- Adversarial Reprogramming of Pretrained Neural Networks for Fraud Detection (CIKM 2021)
  - Lingwei Chen, Yujie Fan, Yanfang Ye
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3459637.3482053)

- Fine-Grained Element Identification in Complaint Text of Internet Fraud (CIKM 2021)
  - Tong Liu, Siyuan Wang, Jingchao Fu, Lei Chen, Zhongyu Wei, Yaqi Liu, Heng Ye, Liaosa Xu, Weiqiang Wang, Xuanjing Huang
  - Paper  (https://arxiv.org/abs/2108.08676)

- Could You Describe the Reason for the Transfer: A Reinforcement Learning Based Voice-Enabled Bot Protecting Customers from Financial Frauds (CIKM 2021)
  - Zihao Wang, Fudong Wang, Haipeng Zhang, Minghui Yang, Shaosheng Cao, Zujie Wen, Zhe Zhang
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3459637.3481906)

- Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network (IJCAI 2021)
  - Wangli Lin, Li Sun, Qiwei Zhong, Can Liu, Jinghua Feng, Xiang Ao, Hao Yang
  - Paper  (https://www.ijcai.org/proceedings/2021/505)

- Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection (KDD 2021)
  - Can Liu, Li Sun, Xiang Ao, Jinghua Feng, Qing He, Hao Yang
  - Paper  (https://dl.acm.org/doi/10.1145/3447548.3467142)

- Live-Streaming Fraud Detection: A Heterogeneous Graph Neural Network Approach (KDD 2021)
  - Haishuai Wang, Zhao Li, Peng Zhang, Jiaming Huang, Pengrui Hui, Jian Liao, Ji Zhang, Jiajun Bu
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3447548.3467065)

- Customs Fraud Detection in the Presence of Concept Drift (IncrLearn@ICDM 2021)
  - Tung-Duong Mai, Kien Hoang, Aitolkyn Baigutanova, Gaukhartas Alina, Sundong Kim
  - Paper  (https://arxiv.org/abs/2109.14155)
  
- Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (WWW 2021)
  - Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3442381.3449989)

2020
  
- Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection (AAAI 2020)
  - Dawei Cheng, Sheng Xiang, Chencheng Shang, Yiyi Zhang, Fangzhou Yang, Liqing Zhang
  - Paper  (https://aaai.org/Papers/AAAI/2020GB/AISI-ChengD.87.pdf)

- FlowScope: Spotting Money Laundering Based on Graphs (AAAI 2020)
  - Xiangfeng Li, Shenghua Liu, Zifeng Li, Xiaotian Han, Chuan Shi, Bryan Hooi, He Huang, Xueqi Cheng
  - Paper  (https://shenghua-liu.github.io/papers/aaai2020cr-flowscope.pdf)
  - Code  (https://github.com/aplaceof/FlowScope)

- Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (CIKM 2020)
  - Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, Philip S. Yu
  - Paper  (https://arxiv.org/abs/2008.08692)
  - Code  (https://github.com/YingtongDou/CARE-GNN)

- Loan Default Analysis with Multiplex Graph Learning (CIKM 2020)
  - Binbin Hu, Zhiqiang Zhang, Jun Zhou, Jingli Fang, Quanhui Jia, Yanming Fang, Quan Yu, Yuan Qi
  - Paper  (https://www.researchgate.net/publication/343626706_Loan_Default_Analysis_with_Multiplex_Graph_Learning)

- Error-Bounded Graph Anomaly Loss for GNNs (CIKM 2020)
  - Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang, Meng Jiang
  - Paper  (http://www.meng-jiang.com/pubs/gal-cikm20/gal-cikm20-paper.pdf)
  - Code  (https://github.com/zhao-tong/Graph-Anomaly-Loss)
  
- BotSpot: A Hybrid Learning Framework to Uncover Bot Install Fraud in Mobile Advertising (CIKM 2020)
  - Tianjun Yao, Qing Li, Shangsong Liang, Yadong Zhu
  - Paper  (https://dl.acm.org/doi/pdf/10.1145/3340531.3412690)
  - Code  (https://github.com/akakeigo2020/CIKM-Applied_Research-2150)

- Early Fraud Detection with Augmented Graph Learning (DLG@KDD 2020)
  - Tong Zhao, Bo Ni, Wenhao Yu, Meng Jiang
  - Paper  (http://www.meng-jiang.com/pubs/earlyfraud-dlg20/earlyfraud-dlg20-paper.pdf)

- NAG: Neural Feature Aggregation Framework for Credit Card Fraud Detection (ICDM 2020)
  - Kanishka Ghosh Dastidar, Johannes Jurgovsky, Wissam Siblini, Liyun He-Guelton, Michael Granitzer
  - Paper  (https://www.computer.org/csdl/proceedings-article/icdm/2020/831600a092/1r54A3Sb2yk)

- Heterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection (ICDM 2020)
  - Yong-Nan Zhu, Xiaotian Luo, Yu-Feng Li, Bin Bu, Kaibo Zhou, Wenbin Zhang, Mingfan Lu
  - Paper  (https://cs.nju.edu.cn/liyf/paper/icdm20-hmgnn.pdf)
  
- Collaboration Based Multi-Label Propagation for Fraud Detection (IJCAI 2020)
  - Haobo Wang, Zhao Li, Jiaming Huang, Pengrui Hui, Weiwei Liu, Tianlei Hu, Gang Chen
  - Paper  (https://www.ijcai.org/Proceedings/2020/343)

- The Behavioral Sign of Account Theft: Realizing Online Payment Fraud Alert (IJCAI 2020)
  - Cheng Wang
  - Paper  (https://www.ijcai.org/Proceedings/2020/0636.pdf)

- Federated Meta-Learning for Fraudulent Credit Card Detection (IJCAI 2020)
  - Wenbo Zheng, Lan Yan, Chao Gou, Fei-Yue Wang
  - Paper  (https://www.ijcai.org/Proceedings/2020/642)

- Robust Spammer Detection by Nash Reinforcement Learning (KDD 2020)
  - Yingtong Dou, Guixiang Ma, Philip S. Yu, Sihong Xie
  - Paper  (https://arxiv.org/abs/2006.06069)
  - Code  (https://github.com/YingtongDou/Nash-Detect)
  
- DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection (KDD 2020)
  - Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha
  - Paper  (https://seondong.github.io/assets/papers/2020_KDD_DATE.pdf)
  - Code  (https://github.com/Roytsai27/Dual-Attentive-Tree-aware-Embedding)

- Fraud Transactions Detection via Behavior Tree with Local Intention Calibration (KDD 2020)
  - Can Liu, Qiwei Zhong, Xiang Ao, Li Sun, Wangli Lin, Jinghua Feng, Qing He, Jiayu Tang
  - Paper  (https://dl.acm.org/doi/pdf/10.1145/3394486.3403354)

- Interleaved Sequence RNNs for Fraud Detection (KDD 2020)
  - Bernardo Branco, Pedro Abreu, Ana Sofia Gomes, Mariana S. C. Almeida, João Tiago Ascensão, Pedro Bizarro
  - Paper  (https://arxiv.org/abs/2002.05988)

- GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection (SIGIR 2020)
  - Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui
  - Paper  (https://arxiv.org/abs/2005.10150)
 
- Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection (SIGIR 2020)
  - Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng
  - Paper  (https://arxiv.org/abs/2005.00625)
  - Code  (https://github.com/safe-graph/DGFraud)
  
- Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks (WWW 2020)
  - Adam Breuer, Roee Eilat, Udi Weinsberg
  - Paper  (https://arxiv.org/abs/2004.04834)

- Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network (WWW 2020)
  - Qiwei Zhong, Yang Liu, Xiang Ao, Binbin Hu, Jinghua Feng, Jiayu Tang, Qing He
  - Paper  (https://dl.acm.org/doi/abs/10.1145/3366423.3380159)

- ASA: Adversary Situation Awareness via Heterogeneous Graph Convolutional Networks (WWW 2020)
  - Rui Wen, Jianyu Wang, Chunming Wu, Jian Xiong
  - Paper  (https://dl.acm.org/doi/10.1145/3366424.3391266)
  
- Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection (WWW 2020)
  - Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, Qing He
  - Paper  (https://dl.acm.org/doi/fullHtml/10.1145/3366423.3380172)
  
2019
- SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs (DSAA 2019)
  - Hamed Nilforoshan, Neil Shah
  - Paper  (https://arxiv.org/abs/1908.07087)
  - Code  (https://github.com/hamedn/SliceNDice)

- FARE: Schema-Agnostic Anomaly Detection in Social Event Logs (DSAA 2019)
  - Neil Shah
  - Paper  (http://nshah.net/publications/FARE.DSAA.19.pdf)
  
- Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism (AAAI 2019)
  - Binbin Hu, Zhiqiang Zhang, Chuan Shi, Jun Zhou, Xiaolong Li, Yuan Qi
  - Paper  (https://aaai.org/ojs/index.php/AAAI/article/view/3884)
  - Code  (https://github.com/safe-graph/DGFraud)
  
- GeniePath: Graph Neural Networks with Adaptive Receptive Paths (AAAI 2019)
  - Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi
  - Paper  (https://arxiv.org/abs/1802.00910)
  - Code  (https://github.com/safe-graph/DGFraud)
  
- SAFE: A Neural Survival Analysis Model for Fraud Early Detection (AAAI 2019)
  - Panpan Zheng, Shuhan Yuan, Xintao Wu
  - Paper  (https://arxiv.org/abs/1809.04683v2)
  - Code  (https://github.com/PanpanZheng/SAFE)

- One-Class Adversarial Nets for Fraud Detection (AAAI 2019)
  - Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu
  - Paper  (https://arxiv.org/abs/1803.01798)
  - Code  (https://github.com/ILoveAI2019/OCAN)
  
- Uncovering Download Fraud Activities in Mobile App Markets (ASONAM 2019)
  - Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu
  - Paper  (https://arxiv.org/pdf/1907.03048.pdf)

- Spam Review Detection with Graph Convolutional Networks (CIKM 2019)
  - Ao Li, Zhou Qin, Runshi Liu, Yiqun Yang, Dong Li
  - Paper  (https://arxiv.org/abs/1908.10679)
  - Code  (https://github.com/safe-graph/DGFraud)

- Key Player Identification in Underground Forums Over Attributed Heterogeneous Information Network Embedding Framework (CIKM 2019)
   - Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Chuan Shi
   - Paper  (http://mason.gmu.edu/~lzhao9/materials/papers/lp0110-zhangA.pdf)
   - Code  (https://github.com/safe-graph/DGFraud)
   
- CatchCore: Catching Hierarchical Dense Subtensor (ECML-PKDD 2019)
  -  Wenjie Feng, Shenghua Liu, Huawei Shen, and Xueqi Cheng
  - Paper  (https://shenghua-liu.github.io/papers/pkdd2019-catchcore.pdf)
  - Code  (https://github.com/wenchieh/catchcore)
  
- Spotting Collective Behaviour of Online Frauds in Customer Reviews (IJCAI 2019)
  - Sarthika Dhawan, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy Chakraborty
  - Paper  (https://arxiv.org/abs/1905.13649)
  - Code  (https://github.com/LCS2-IIITD/DeFrauder)
  
- A Semi-Supervised Graph Attentive Network for Fraud Detection (ICDM 2019)
  - Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Qi Yuan 
  - Paper  (https://arxiv.org/abs/2003.01171)
  - Code  (https://github.com/safe-graph/DGFraud)
 
- EigenPulse: Detecting Surges in Large Streaming Graphs with Row Augmentation (PAKDD 2019)
  - Jiabao Zhang, Shenghua Liu, Wenjian Yu, Wenjie Feng, Xueqi Cheng
  - Paper  (https://shenghua-liu.github.io/papers/pakdd2019-eigenpulse.pdf)

- Uncovering Insurance Fraud Conspiracy with Network Learning (SIGIR 2019)
  - Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi
  - Paper  (https://dl.acm.org/citation.cfm?id=3331372)
  
- A Contrast Metric for Fraud Detection in Rich Graphs (TKDE 2019)
  - Shenghua Liu, Bryan Hooi, Christos Faloutsos
  - Paper  (https://shenghua-liu.github.io/papers/tkde2019-constrastsusp_holoscope.pdf)

- Think Outside the Dataset: Finding Fraudulent Reviews using Cross-Dataset Analysis (WWW 2019)
  - Shirin Nilizadeh, Hojjat Aghakhani, Eric Gustafson, Christopher Kruegel, Giovanni Vigna
  - Paper  (https://www.researchgate.net/publication/333060486_Think_Outside_the_Dataset_Finding_Fraudulent_Reviews_using_Cross-Dataset_Analysis)

- Securing the Deep Fraud Detector in Large-Scale E-Commerce Platform via Adversarial Machine Learning Approach (WWW 2019)
  - Qingyu Guo, Zhao Li, Bo An, Pengrui Hui, Jiaming Huang, Long Zhang, Mengchen Zhao
  - Paper  (https://www.ntu.edu.sg/home/boan/papers/WWW19.pdf)

- No Place to Hide: Catching Fraudulent Entities in Tensors (WWW 2019)
  - Yikun Ban, Xin Liu, Ling Huang, Yitao Duan, Xue Liu, Wei Xu
  - Paper  (https://arxiv.org/pdf/1810.06230.pdf)
  
- FdGars: Fraudster Detection via Graph Convolutional Networks in Online App Review System (WWW 2019)
  - Rui Wen, Jianyu Wang and Yu Huang
  - Paper  (https://dl.acm.org/citation.cfm?id=3316586)
  - Code  (https://github.com/safe-graph/DGFraud)

2018

- Heterogeneous Graph Neural Networks for Malicious Account Detection (CIKM 2018)
  - Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song
  - Paper  (https://dl.acm.org/doi/10.1145/3269206.3272010)
  - Code  (https://github.com/safe-graph/DGFraud)
  
- Reinforcement Mechanism Design for Fraudulent Behaviour in e-Commerce (AAAI 2018)
  - Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, Yiwei Zhang
  - Paper  (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16650)
  
- Adapting to Concept Drift in Credit Card Transaction Data Streams Using Contextual Bandits and Decision Trees (AAAI 2018)
  - Dennis J. N. J. Soemers, Tim Brys, Kurt Driessens, Mark H. M. Winands, Ann Nowé
  - Paper  (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16183/16394)

- Nextgen AML: Distributed Deep Learning Based Language Technologies to Augment Anti Money Laundering Investigation(ACL 2018)
  - Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, Dadong Wan
  - Paper  (https://www.aclweb.org/anthology/P18-4007)

- Preserving Privacy of Fraud Detection Rule Sharing Using Intel's SGX (CIKM 2018)
  - Daniel Deutch, Yehonatan Ginzberg, Tova Milo
  - Paper  (https://www.researchgate.net/publication/328439345_Preserving_Privacy_of_Fraud_Detection_Rule_Sharing_Using_Intel%27s_SGX)

- Deep Structure Learning for Fraud Detection (ICDM 2018)
  - Haibo Wang, Chuan Zhou, Jia Wu, Weizhen Dang, Xingquan Zhu, Jilong Wang
  - Paper  (https://www.researchgate.net/publication/330030140_Deep_Structure_Learning_for_Fraud_Detection)

- Learning Sequential Behavior Representations for Fraud Detection (ICDM 2018)
  - Jia Guo, Guannan Liu, Yuan Zuo, Junjie Wu
  - Paper  (https://www.researchgate.net/publication/330028902_Learning_Sequential_Behavior_Representations_for_Fraud_Detection)

- Impression Allocation for Combating Fraud in E-commerce Via Deep Reinforcement Learning with Action Norm Penalty (IJCAI 2018)
  - Mengchen Zhao, Zhao Li, Bo An, Haifeng Lu, Yifan Yang, Chen Chu
  - Paper  (https://www.ijcai.org/proceedings/2018/0548.pdf)

- Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach (KDD 2018)
  - Daniel de Roux, Boris Perez, Andrés Moreno, María-Del-Pilar Villamil, César Figueroa
  - Paper  (https://www.kdd.org/kdd2018/accepted-papers/view/tax-fraud-detection-for-under-reporting-declarations-using-an-unsupervised-)
  
- Collective Fraud Detection Capturing Inter-Transaction Dependency (KDD 2018)
  - Bokai Cao, Mia Mao, Siim Viidu, Philip Yu
  - Paper  (http://proceedings.mlr.press/v71/cao18a.html)
  
- Fraud Detection with Density Estimation Trees (KDD 2018)
  - Fraud Detection with Density Estimation Trees
  - Paper  (http://proceedings.mlr.press/v71/ram18a/ram18a.pdf)

- Real-time Constrained Cycle Detection in Large Dynamic Graphs (VLDB 2018)
  - Xiafei Qiu, Wubin Cen, Zhengping Qian, You Peng, Ying Zhang, Xuemin Lin, Jingren Zhou
  - Paper  (http://www.vldb.org/pvldb/vol11/p1876-qiu.pdf)
  
- REV2: Fraudulent User Prediction in Rating Platforms (WSDM 2018)
  - Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, V. S. Subrahmanian
  - Paper  (https://cs.stanford.edu/~srijan/pubs/rev2-wsdm18.pdf)
  - Code  (https://cs.stanford.edu/~srijan/rev2/)
  
- Exposing Search and Advertisement Abuse Tactics and Infrastructure of Technical Support Scammers (WWW 2018)
  - Bharat Srinivasan, Athanasios Kountouras, Najmeh Miramirkhani, Monjur Alam, Nick Nikiforakis, Manos Antonakakis, Mustaque Ahamad
  - Paper  (https://www.securitee.org/files/tss_www2018.pdf)

2017
- ZooBP: Belief Propagation for Heterogeneous Networks (VLDB 2017)
  - Dhivya Eswaran, Stephan Gunnemann, Christos Faloutsos, Disha Makhija, Mohit Kumar
  - Paper  (http://www.vldb.org/pvldb/vol10/p625-eswaran.pdf)
  - Code  (https://github.com/safe-graph/UGFraud)

- Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond (AAAI 2017)
  - Parisa Kaghazgaran, James Caverlee, Majid Alfifi
  - Paper  (https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15659)
  
- Detection of Money Laundering Groups: Supervised Learning on Small Networks (AAAI 2017)
  - David Savage, Qingmai Wang, Xiuzhen Zhang, Pauline Chou, Xinghuo Yu
  - Paper  (https://arxiv.org/pdf/1608.00708.pdf)
  
- Spectrum-based Deep Neural Networks for Fraud Detection (CIKM 2017)
  - Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu
  - Paper  (https://arxiv.org/abs/1706.00891)

- HoloScope: Topology-and-Spike Aware Fraud Detection (CIKM 2017)
  - Shenghua Liu, Bryan Hooi, Christos Faloutsos
  - Paper  (https://arxiv.org/abs/1705.02505)

- The Many Faces of Link Fraud (ICDM 2017)
  - Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos
  - Paper  (https://arxiv.org/abs/1704.01420)

- HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks (ICDM 2017)
  - Bokai Cao, Mia Mao, Siim Viidu, Philip S. Yu
  - Paper  (https://arxiv.org/abs/1709.04129)
  
- GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (ICDM 2017)
  - Binghui Wang, Neil Zhenqiang Gong, Hao Fu
  - Paper  (https://ieeexplore.ieee.org/document/8215519)
  - Code  (https://github.com/safe-graph/UGFraud)
  
- Improving Card Fraud Detection Through Suspicious Pattern Discovery (IEA/AIE 2017)
  - Fabian Braun, Olivier Caelen, Evgueni N. Smirnov, Steven Kelk, Bertrand Lebichot:
  - Paper  (http://www.oliviercaelen.be/doc/GBSSCCFDS.pdf)

- Online Reputation Fraud Campaign Detection in User Ratings (IJCAI 2017)
  - Chang Xu, Jie Zhang, Zhu Sun
  - Paper  (https://www.ijcai.org/proceedings/2017/0541.pdf)
  
- Uncovering Unknown Unknowns in Financial Services Big Data by Unsupervised Methodologies: Present and Future trends (KDD 2017)
  - Gil Shabat, David Segev, Amir Averbuch
  - Paper  (http://proceedings.mlr.press/v71/shabat18a.html)
  
- PD-FDS: Purchase Density based Online Credit Card Fraud Detection System (KDD 2017)
  - Youngjoon Ki, Ji Won Yoon 
  - Paper  (http://proceedings.mlr.press/v71/ki18a/ki18a.pdf)
  
- HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection (SDM 2017)
  - Si Zhang, Dawei Zhou, Mehmet Yigit Yildirim, Scott Alcorn, Jingrui He, Hasan Davulcu, Hanghang Tong
  - Paper  (http://www.public.asu.edu/~hdavulcu/SDM17.pdf)

2016
- A Fraud Resilient Medical Insurance Claim System (AAAI 2016)
  - Yuliang Shi, Chenfei Sun, Qingzhong Li, Lizhen Cui, Han Yu, Chunyan Miao
  - Paper  (https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11813)

- A Graph-Based, Semi-Supervised, Credit Card Fraud Detection System (COMPLEX NETWORKS 2016)
  - Bertrand Lebichot, Fabian Braun, Olivier Caelen, Marco Saerens
  - Paper  (http://www.oliviercaelen.be/doc/IEAAIE_2017_Finalversion-PDF_39.pdf)

- FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (KDD 2016)
  - Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos
  - Paper  (https://www.andrew.cmu.edu/user/bhooi/papers/fraudar_kdd16.pdf)
  - Code  (https://github.com/safe-graph/UGFraud)
  
- Identifying Anomalies in Graph Streams Using Change Detection (KDD 2016)
  - William Eberle and Lawrence Holde
  - Paper  (http://www.mlgworkshop.org/2016/paper/MLG2016_paper_12.pdf)

- FairPlay: Fraud and Malware Detection in Google Play (SDM 2016)
  - Mahmudur Rahman, Mizanur Rahman, Bogdan Carbunar, Duen Horng Chau
  - Paper  (https://arxiv.org/abs/1703.02002)

- BIRDNEST: Bayesian Inference for Ratings-Fraud Detection (SDM 2016)
  - Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
  - Paper  (https://www.andrew.cmu.edu/user/bhooi/papers/birdnest_sdm16.pdf)

- Understanding the Detection of View Fraud in Video Content Portals (WWW 2016)
  - Miriam Marciel, Rubén Cuevas, Albert Banchs, Roberto Gonzalez, Stefano Traverso, Mohamed Ahmed, Arturo Azcorra
  - Paper  (https://dl.acm.org/citation.cfm?id=2882980)

2015
- Toward An Intelligent Agent for Fraud Detection — The CFE Agent (AAAI 2015)
  - Joe Johnson
  - Paper  (https://www.aaai.org/ocs/index.php/FSS/FSS15/paper/download/11664/11485)
  
- Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data (AAAI 2015)
  - Juan Liu, Eric Bier, Aaron Wilson, Tomonori Honda, Kumar Sricharan, Leilani Gilpin, John Alexis Guerra Gómez, Daniel Davies
  - Paper  (https://pdfs.semanticscholar.org/1ea7/125b789ef938bffe10c7588e6b071c4ff73c.pdf)

- Robust System for Identifying Procurement Fraud (AAAI 2015)
  - Amit Dhurandhar, Rajesh Kumar Ravi, Bruce Graves, Gopikrishnan Maniachari, Markus Ettl
  - Paper  (https://pdfs.semanticscholar.org/27af/c9ec453ae0cf9e55f4032ff688cb70c2a61e.pdf)

- Fraud Transaction Recognition: A Money Flow Network Approach (CIKM 2015)
  - Renxin Mao, Zhao Li, Jinhua Fu
  - Paper  (https://dl.acm.org/citation.cfm?id=2806647)

- Towards Collusive Fraud Detection in Online Reviews (ICDM 2015)
  - Chang Xu, Jie Zhang
  - Paper  (https://ieeexplore.ieee.org/document/7373434)

- Catch the Black Sheep: Unified Framework for Shilling Attack Detection Based on Fraudulent Action Propagation (IJCAI 2015)
  - Yongfeng Zhang, Yunzhi Tan, Min Zhang, Yiqun Liu, Tat-Seng Chua, Shaoping Ma
  - Paper  (https://www.ijcai.org/Proceedings/15/Papers/341.pdf)

- Collective Opinion Spam Detection: Bridging Review Networks and Metadata (KDD 2015)
  - Shebuti Rayana, Leman Akoglu
  - Paper  (https://www.andrew.cmu.edu/user/lakoglu/pubs/15-kdd-collectiveopinionspam.pdf)
  - Code  (https://github.com/safe-graph/UGFraud)

- Graph-Based User Behavior Modeling: From Prediction to Fraud Detection (KDD 2015)
  - Alex Beutel, Leman Akoglu, Christos Faloutsos
  - Paper  (https://www.cs.cmu.edu/~abeutel/kdd2015_tutorial/tutorial.pdf)

- FrauDetector: A Graph-Mining-based Framework for Fraudulent Phone Call Detection (KDD 2015)
  - Vincent S. Tseng, Jia-Ching Ying, Che-Wei Huang, Yimin Kao, Kuan-Ta Chen
  - Paper  (http://repository.ncku.edu.tw/bitstream/987654321/166322/1/4010204000-000004_1.pdf)
  
- A Framework for Intrusion Detection Based on Frequent Subgraph Mining (SDM 2015)
  - Vitali Herrera-Semenets, Niusvel Acosta-Mendoza, Andres Gago-Alonso
  - Paper  (https://www.researchgate.net/publication/271839253_A_Framework_for_Intrusion_Detection_based_on_Frequent_Subgraph_Mining)

- Crowd Fraud Detection in Internet Advertising (WWW 2015)
  - Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, Tong Zhang
  - Paper  (http://www.www2015.it/documents/proceedings/proceedings/p1100.pdf)

2014
- Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective (ICDM 2014)
  - Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos
  - Paper  (https://arxiv.org/pdf/1410.3915.pdf)
  - Code  (https://github.com/safe-graph/UGFraud)

- Fraudulent Support Telephone Number Identification Based on Co-Occurrence Information on the Web (AAAI 2014)
  - Xin Li, Yiqun Liu, Min Zhang, Shaoping Ma
  - Paper  (https://pdfs.semanticscholar.org/2733/1f48c87736ea12b9edec062e384d3bd58f88.pdf)

- Corporate Residence Fraud Detection (KDD 2014)
  - Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens
  - Paper  
(http://delivery.acm.org/10.1145/2630000/2623333/p1650-fortuny.pdf?ip=129.215.164.203&id=2623333&acc=ACTIVE%20SERVICE&key=C2D842D97AC95F7A%2EEB9E991028F4E1F1%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1559048806_f1a6f763ef7088a4fb4b1a4ff94856f
8)

- Graphical Models for Identifying Fraud and Waste in Healthcare Claims (SDM 2014)
  - Peder A. Olsen, Ramesh Natarajan, Sholom M. Weiss
  - Paper  (https://epubs.siam.org/doi/abs/10.1137/1.9781611973440.66)

- Improving Credit Card Fraud Detection with Calibrated Probabilities (SDM 2014)
  - Alejandro Correa Bahnsen, Aleksandar Stojanovic, Djamila Aouada, Björn E. Ottersten
  - Paper  (https://pdfs.semanticscholar.org/9241/ef2a2f6638eafeffd0056736c0f46f9aa083.pdf)

- Large Graph Mining: Patterns, Cascades, Fraud Detection, and Algorithms (WWW 2014)
  - Christos Faloutsos
  - Paper  (http://wwwconference.org/proceedings/www2014/proceedings/p1.pdf)
  
2013

- Opinion Fraud Detection in Online Reviews by Network Effects (AAAI 2013)
  - Leman Akoglu, Rishi Chandy, Christos Faloutsos
  - Paper  (https://www.researchgate.net/publication/279905898_Opinion_fraud_detection_in_online_reviews_by_network_effects)
  
- Using Social Network Knowledge for Detecting Spider Constructions in Social Security Fraud (ASONAM 2013)
  - Véronique Van Vlasselaer, Jan Meskens, Dries Van Dromme, Bart Baesens 
  - Paper  (https://ieeexplore.ieee.org/document/6785796)
  
- Ranking Fraud Detection for Mobile Apps: a Holistic View (CIKM 2013)
  - Hengshu Zhu, Hui Xiong, Yong Ge, Enhong Chen
  - Paper  (http://dm.ustc.edu.cn/zhu-cikm13.pdf)

- Using Co-Visitation Networks for Detecting Large Scale Online Display Advertising Exchange Fraud (KDD 2013)
  - Ori Stitelman, Claudia Perlich, Brian Dalessandro, Rod Hook, Troy Raeder, Foster J. Provost
  - Paper  (http://chbrown.github.io/kdd-2013-usb/kdd/p1240.pdf)

- Adaptive Adversaries: Building Systems to Fight Fraud and Cyber Intruders (KDD 2013)
  - Ari Gesher
  - Paper  (https://dl.acm.org/citation.cfm?id=2491134)

- Anomaly, Event, and Fraud Detection in Large Network Datasets (WSDM 2013)
  - Leman Akoglu, Christos Faloutsos
  - Paper  (https://www.andrew.cmu.edu/user/lakoglu/wsdm13/13-wsdm-tutorial.pdf)

2012

- Fraud Detection: Methods of Analysis for Hypergraph Data (ASONAM 2012)
  - Anna Leontjeva, Konstantin Tretyakov, Jaak Vilo, and Taavi Tamkivi
  - Paper  (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6425618)

- Online Modeling of Proactive Moderation System for Auction Fraud Detection (WWW 2012)
  - Liang Zhang, Jie Yang, Belle L. Tseng
  - Paper  (http://www.chennaisunday.com/Java%202012%20Base%20Paper/Online%20Modeling%20of%20Proactive%20Moderation%20System%20for%20Auction%20Fraud%20Detection.pdf)

2011
- A Machine-Learned Proactive Moderation System for Auction Fraud Detection (CIKM 2011)
  - Liang Zhang, Jie Yang, Wei Chu, Belle L. Tseng
  - Paper  (http://www.gatsby.ucl.ac.uk/~chuwei/paper/p2501-zhang.pdf)

- A Taxi Driving Fraud Detection System (ICDM 2011)
  - Yong Ge, Hui Xiong, Chuanren Liu, Zhi-Hua Zhou
  - Paper  (https://ieeexplore.ieee.org/document/6137222)

- Utility-Based Fraud Detection (IJCAI 2011)
  - Luís Torgo, Elsa Lopes
  - Paper  (https://www.ijcai.org/Proceedings/11/Papers/255.pdf)

- A Pattern Discovery Approach to Retail Fraud Detection (KDD 2011)
  - Prasad Gabbur, Sharath Pankanti, Quanfu Fan, Hoang Trinh
  - Paper  (http://www2.engr.arizona.edu/~pgsangam/gabbur_kdd_11.pdf)

2010

- Hunting for the Black Swan: Risk Mining from Text (ACL 2010)
  - JL Leidner, F Schilder
  - Paper  (https://www.aclweb.org/anthology/P10-4010)
  
- Fraud Detection by Generating Positive Samples for Classification from Unlabeled Data (ACL 2010)
  - Levente Kocsis, Andras George
  - Paper  (http://www.szit.bme.hu/~gya/publications/KocsisGyorgy.pdf)
  
2009
- SVM-based Credit Card Fraud Detection with Reject Cost and Class-Dependent Error Cost (PAKDD 2009)
  - En-hui Zheng,Chao Zou,Jian Sun, Le Chen
  - Paper  (https://www.semanticscholar.org/paper/SVM-Based-Cost-sensitive-Classification-Algorithm-Zheng-Zou/bcae06626ccd453925ef040a1edb5cbb10b862ef)
  
- An Approach for Automatic Fraud Detection in the Insurance Domain (AAAI 2009)
  - Alexander Widder, Rainer v. Ammon, Gerit Hagemann, Dirk Schönfeld
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.325.3231&rep=rep1&type=pdf)
  
2007
- Relational Data Pre-Processing Techniques for Improved Securities Fraud Detection (KDD 2007)
  - Andrew S. Fast, Lisa Friedland, Marc E. Maier, Brian J. Taylor, David D. Jensen, Henry G. Goldberg, John Komoroske
  - Paper  (https://dl.acm.org/citation.cfm?id=1281192.1281293)

- Uncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder (PKDD 2007)
  - Fletcher Lu
  - Paper  (https://link.springer.com/chapter/10.1007/978-3-540-74976-9_56)

- Netprobe: A Fast and Scalable System for Fraud Detection in Online Auction Networks (WWW 2007)
  - Shashank Pandit, Duen Horng Chau, Samuel Wang, Christos Faloutsos
  - Paper  (http://www.cs.cmu.edu/~christos/PUBLICATIONS/netprobe-www07.pdf)

2006

- Data Mining Approaches to Criminal Career Analysis (ICDM 2006)
  - Jeroen S. De Bruin, Tim K. Cocx, Walter A. Kosters, Jeroen F. J. Laros, Joost N. Kok 
  - Paper  (https://ieeexplore.ieee.org/document/4053045)
  
- Large Scale Detection of Irregularities in Accounting Data (ICDM 2006)
  - Stephen Bay, Krishna Kumaraswamy, Markus G. Anderle, Rohit Kumar, David M. Steier 
  - Paper  (https://ieeexplore.ieee.org/document/4053036)
  
- Camouflaged Fraud Detection in Domains with Complex Relationships (KDD 2006)
  - Sankar Virdhagriswaran, Gordon Dakin
  - Paper  (https://dl.acm.org/citation.cfm?id=1150532)

- Detecting Fraudulent Personalities in Networks of Online Auctioneers (PKDD 2006)
  - Duen Horng Chau, Shashank Pandit, Christos Faloutsos
  - Paper  (http://www.cs.cmu.edu/~dchau/papers/auction_fraud_pkdd06.pdf)

2005

- Technologies to Defeat Fraudulent Schemes Related to Email Requests (AAAI 2005)
  - Edoardo Airoldi, Bradley Malin, and Latanya Sweeney
  - Paper  (http://www.aaai.org/Library/Symposia/Spring/2005/ss05-01-023.php)
  
- AI Technologies to Defeat Identity Theft Vulnerabilities (AAAI 2005)
  - Latanya Sweeney
  - Paper  (https://dataprivacylab.org/dataprivacy/projects/idangel/paper1.pdf)
  
- Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions (ECML 2005)
  - Fletcher Lu, J. Efrim Boritz
  - Paper  (https://faculty.uoit.ca/fletcherlu/LuECML05.pdf)

- Using Relational Knowledge Discovery to Prevent Securities Fraud (KDD 2005)
  - Jennifer Neville, Özgür Simsek, David D. Jensen, John Komoroske, Kelly Palmer, Henry G. Goldberg
  - Paper  (https://www.cs.purdue.edu/homes/neville/papers/neville-et-al-kdd2005.pdf)

2003
- Applying Data Mining in Investigating Money Laundering Crimes (KDD 2003)
  - Zhongfei (Mark) Zhang, John J. Salerno, Philip S. Yu
  - Paper  (https://pdfs.semanticscholar.org/9124/b61d48b7e52008c7fd5fac1b7eac38474581.pdf)

2000
- Document Classification and Visualisation to Support the Investigation of Suspected Fraud (PKDD 2000)
  - Johan Hagman, Domenico Perrotta, Ralf Steinberger, and Aristi de Varfis
  - Paper  (https://pdfs.semanticscholar.org/9124/b61d48b7e52008c7fd5fac1b7eac38474581.pdf)
  
1999
- Statistical Challenges to Inductive Inference in Linked Data. (AISTATS 1999)
  - David Jensen
  - Paper  (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.589.1445&rep=rep1&type=pdf)

1998

- Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection (KDD 1998)
  - Phillip K Chan, Salvatore J Stolfo 
  - Paper  (https://pdfs.semanticscholar.org/6e19/3366945bf3bd72d5ba906e3982ac4d8ae874.pdf)
  
- Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model (NIPS 1998)
  - Jaakko Hollmén, Volker Tresp
  - Paper  (https://papers.nips.cc/paper/1505-call-based-fraud-detection-in-mobile-communication-networks-using-a-hierarchical-regime-switching-model.pdf)

1997

- Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype (ICANN 1997)
  - Yves Moreau, Herman Verrelst, Joos Vandewalle
  - Paper  (https://link.springer.com/content/pdf/10.1007%2FBFb0020294.pdf)
  
- Prospective Assessment of AI Technologies for Fraud Detection: A Case Study (AAAI 1997)
  - David Jensen
  - Paper  (https://pdfs.semanticscholar.org/0efe/8a145cc4d52e8769bb1d13142326a154624f.pdf)
  
- Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results (AAAI 1997)
  - Salvatore J. Stolfo, David W. Fan, Wenke Lee and Andreas L. Prodromidis
  - Paper  (https://pdfs.semanticscholar.org/29b3/e330e0045e5da71cc1d333bed24b7a4670f8.pdf)

1995
- Fraud: Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures (UAI 1995)
  - Kazuo J. Ezawa, Til Schuermann
  - Paper  (https://arxiv.org/abs/1302.4945)
  
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License

- CC0 Universal (https://github.com/benedekrozemberczki/awesome-fraud-detection-papers/blob/master/LICENSE)


frauddetectionpapers Github: https://github.com/benedekrozemberczki/awesome-fraud-detection-papers