{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semi-supervised-credit-card-fraud-detection-1","title":"Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation","arxiv_id":"2412.18287","date":"2024-12-24","proceeding":"AAAI 2023 6","authors":["Sheng Xiang","Mingzhi Zhu","Dawei Cheng","Enxia Li","Ruihui Zhao","Yi Ouyang","Ling Chen","Yefeng Zheng"],"abstract":"Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.","url_abs":"https://arxiv.org/abs/2412.18287v1","url_pdf":"https://arxiv.org/pdf/2412.18287v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semi-supervised-credit-card-fraud-detection-1","repo_url":"https://github.com/ai4risk/antifraud","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"semi-supervised-credit-card-fraud-detection-1","repo_url":"https://github.com/finint/antifraud","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"fraud-detection","task_name":"Fraud Detection"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fraud-detection-on-amazon-fraud","task":"Fraud Detection","dataset":"Amazon-Fraud","model":"GTAN","rank_in_archive_order":2,"of":6,"metrics":{"AUC-ROC":"97.50","Averaged Precision":"89.26"},"uses_additional_data":false},{"leaderboard":"/sota/fraud-detection-on-yelp-fraud","task":"Fraud Detection","dataset":"Yelp-Fraud","model":"GTAN","rank_in_archive_order":3,"of":10,"metrics":{"AUC-ROC":"94.98","Averaged Precision":"82.41"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-amazon-fraud","task":"Node Classification","dataset":"Amazon-Fraud","model":"GTAN","rank_in_archive_order":2,"of":6,"metrics":{"AUC-ROC":"97.50"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-yelpchi","task":"Node Classification","dataset":"Yelp-Fraud","model":"GTAN","rank_in_archive_order":2,"of":9,"metrics":{"AUC-ROC":"94.98"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2412.18287","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}