{"url":"/sota/fraud-detection-on-amazon-fraud","task":{"name":"Fraud Detection","url":"/task/fraud-detection","note":null},"dataset":{"name":"Amazon-Fraud","url":"/dataset/amazon-fraud"},"category":"Miscellaneous","categories":["Miscellaneous"],"category_note":null,"description":"**Fraud Detection** is a vital topic that applies to many industries including the financial sectors, banking, government agencies, insurance, and law enforcement, and more. Fraud endeavors have detected a radical rise in current years, creating this topic more critical than ever. Despite struggles on the part of the troubled organizations, hundreds of millions of dollars are wasted to fraud each year. Because nearly a few samples confirm fraud in a vast community, locating these can be complex. Data mining and statistics help to predict and immediately distinguish fraud and take immediate action to minimize costs.\r\n\r\n\r\n<span class=\"description-source\">Source: [Applying support vector data description for fraud detection ](https://arxiv.org/abs/2006.00618)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["AUC-ROC","Averaged Precision","F1 Macro","G-mean"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"AUC-ROC":"higher","Averaged Precision":"higher","F1 Macro":"higher","G-mean":null}},"counts":{"rows":6,"rows_with_code":5,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"LEX-GNN","metrics":{"AUC-ROC":"97.91","Averaged Precision":"92.18","F1 Macro":"93.48","G-mean":"92.03"},"uses_additional_data":false,"paper_date":"2024-10-21","paper":"/paper/lex-gnn-label-exploring-graph-neural-network","paper_url":"https://dl.acm.org/doi/10.1145/3627673.3679956","paper_title":"LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection","code":"https://github.com/wdhyun/LEX-GNN","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"GTAN","metrics":{"AUC-ROC":"97.50","Averaged Precision":"89.26"},"uses_additional_data":false,"paper_date":"2024-12-24","paper":"/paper/semi-supervised-credit-card-fraud-detection-1","paper_url":"https://arxiv.org/abs/2412.18287v1","paper_title":"Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation","code":"https://github.com/ai4risk/antifraud","n_code_links":2,"syntology":null},{"rank_in_archive_order":3,"model":"RLC-GNN","metrics":{"AUC-ROC":"97.48"},"uses_additional_data":false,"paper_date":"2021-06-18","paper":"/paper/rlc-gnn-an-improved-deep-architecture-for","paper_url":"https://www.mdpi.com/2076-3417/11/12/5656/htm","paper_title":"RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"RioGNN","metrics":{"AUC-ROC":"96.19"},"uses_additional_data":false,"paper_date":"2021-04-16","paper":"/paper/reinforced-neighborhood-selection-guided","paper_url":"https://arxiv.org/abs/2104.07886v2","paper_title":"Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks","code":"https://github.com/safe-graph/RioGNN","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"PC-GNN","metrics":{"AUC-ROC":"95.86","Averaged Precision":"85.49"},"uses_additional_data":false,"paper_date":"2021-04-19","paper":"/paper/pick-and-choose-a-gnn-based-imbalanced","paper_url":"https://dl.acm.org/doi/abs/10.1145/3442381.3449989","paper_title":"Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection","code":"https://github.com/PonderLY/PC-GNN","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"CARE-GNN","metrics":{"AUC-ROC":"89.73","Averaged Precision":"82.19"},"uses_additional_data":false,"paper_date":"2020-08-19","paper":"/paper/enhancing-graph-neural-network-based-fraud","paper_url":"https://arxiv.org/abs/2008.08692v1","paper_title":"Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters","code":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/caregnn","n_code_links":6,"syntology":{"n_ran":1,"n_unverified":16,"n_samples":17,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":1,"n_unverified":16,"n_samples":17,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":1,"n_unverified":16,"n_samples":17,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}