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A Payment Transaction Pre-training Model for Fraud Transaction Detection

Published: 21 October 2024 Publication History

Abstract

The surge in merchant fraud poses a significant threat to market order and consumer security. Effective security monitoring for merchants is crucial in safeguarding the digital life ecosystem and users' financial well-being. Detecting daily fraudulent payment transactions, a challenging task for current methods, requires efficient transformation of transactions into embeddings, especially in representing merchants based on their behavioral transactions. To address this, we propose the Grouping Sampling-based Sequence Generation (GSSG) method to generate meaningful sequences, enabling interactions among correlated transactions. We introduce Hierarchical Embedding Learning (HEL) and Hierarchical Masking pre-training (HMP) for the effective representation of hierarchical structures within flat transaction sequences. Pretrained on WeChat Pay data, our model, PTP, demonstrates superior performance in downstream fraud transaction detection, especially in few-shot learning scenarios, showcasing great potential in payment transaction scenarios.

References

[1]
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150 (2020).
[2]
Richard J Bolton and David J Hand. 2002. Statistical fraud detection: A review. Statistical science, Vol. 17, 3 (2002), 235--255.
[3]
Shaosheng Cao, XinXing Yang, Cen Chen, Jun Zhou, Xiaolong Li, and Yuan Qi. 2019. Titant: Online real-time transaction fraud detection in ant financial. arXiv preprint arXiv:1906.07407 (2019).
[4]
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. 2019. Class-balanced loss based on effective number of samples. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9268--9277.
[5]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[6]
Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232.
[7]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).
[8]
Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He, and Ling Liu. 2023. BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection. In Proceedings of the ACM Web Conference 2023. 2189--2197.
[9]
Xiaoya Li, Xiaofei Sun, Yuxian Meng, Junjun Liang, Fei Wu, and Jiwei Li. 2019. Dice loss for data-imbalanced NLP tasks. arXiv preprint arXiv:1911.02855 (2019).
[10]
Can Liu, Yuncong Gao, Li Sun, Jinghua Feng, Hao Yang, and Xiang Ao. 2022. User Behavior Pre-training for Online Fraud Detection. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3357--3365.
[11]
Can Liu, Li Sun, Xiang Ao, Jinghua Feng, Qing He, and Hao Yang. 2021. Intention-aware heterogeneous graph attention networks for fraud transactions detection. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3280--3288.
[12]
Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. 2018. Heterogeneous graph neural networks for malicious account detection. In Proceedings of the 27th ACM international conference on information and knowledge management. 2077--2085.
[13]
Rimpal R Popat and Jayesh Chaudhary. 2018. A survey on credit card fraud detection using machine learning. In 2018 2nd international conference on trends in electronics and informatics (ICOEI). IEEE, 1120--1125.
[14]
Mike Schuster and Kuldip K Paliwal. 1997. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, Vol. 45, 11 (1997), 2673--2681.
[15]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017).
[16]
Petar Velivcković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).
[17]
Chuhan Wu, Fangzhao Wu, Tao Qi, Jianxun Lian, Yongfeng Huang, and Xing Xie. 2020. Ptum: Pre-training user model from unlabeled user behaviors via self-supervision. arXiv preprint arXiv:2010.01494 (2020).
[18]
Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, and Xing Xie. 2021. Userbert: Contrastive user model pre-training. arXiv preprint arXiv:2109.01274 (2021).
[19]
Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, and Qing He. 2021. Modeling the field value variations and field interactions simultaneously for fraud detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 14957--14965.
[20]
Dongbo Xi, Fuzhen Zhuang, Bowen Song, Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, and Qing He. 2020. Neural hierarchical factorization machines for user's event sequence analysis. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 1893--1896.
[21]
Panpan Zheng, Shuhan Yuan, and Xintao Wu. 2019. Safe: A neural survival analysis model for fraud early detection. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 1278--1285.
[22]
Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, and Qing He. 2020. Modeling users? behavior sequences with hierarchical explainable network for cross-domain fraud detection. In Proceedings of The Web Conference 2020. 928--938.
[23]
Yong-Nan Zhu, Xiaotian Luo, Yu-Feng Li, Bin Bu, Kaibo Zhou, Wenbin Zhang, and Mingfan Lu. 2020. Heterogeneous mini-graph neural network and its application to fraud invitation detection. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 891--899.

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      cover image ACM Conferences
      CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
      October 2024
      5705 pages
      ISBN:9798400704369
      DOI:10.1145/3627673
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 21 October 2024

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      Author Tags

      1. fraud detection
      2. fraud merchant detection
      3. fraud transaction detection
      4. payment transaction
      5. pre-training model

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      Funding Sources

      • NSFC
      • National Key RD program of China
      • Guangdong Provincial Natural Science Foundation
      • Shenzhen Research Foundation for Basic Re- search
      • University Stability Support program of Shenzhen

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