{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:28:02Z","timestamp":1786512482901,"version":"build-2736575974"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Fraud user identification in telecommunications is hindered by scarce, noisy, and imbalanced labels, while expert rules may provide ambiguous or contradictory evidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose DKFraudNet, a knowledge-guided framework that integrates domain knowledge regularization, an attention-adaptive conditional generative adversarial network, and virtual category learning. Expert rules are organized into a Deterministic-Ambiguous-Contradictory evidence taxonomy for controlled pseudo-labeling. Class-conditioned augmentation alleviates data imbalance, and kernel-based similarity refines ambiguous samples. The framework was evaluated using two real-world city-level telecommunications datasets containing 59,015 and 52,183 users, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DKFraudNet consistently improved downstream classification performance under weak supervision. On the independent City 2 validation set, the CatBoost instantiation achieved an accuracy of 0.911 and an F1-score of 0.909. For operational review prioritization, DKFraudNet-XGBoost required reviewing 40.2% of unverified users to capture 80% of fraud users, corresponding to a 49.71% workload reduction relative to random review, with an AUPRC of 0.954. In operator-side blind verification, genuine-member identification and false-member screening achieved accuracies of 98.9% and 93.3%, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The results show that combining structured domain evidence, adaptive generative augmentation, and uncertainty-aware refinement improves robustness, data efficiency, and operational usefulness for fraud detection under weak supervision.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdata.2026.1887075","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:41:52Z","timestamp":1786509712000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DKFraudNet: a knowledge-guided adversarial learning framework for fraud user detection"],"prefix":"10.3389","volume":"9","author":[{"given":"Yingjun","family":"Shen","sequence":"first","affiliation":[{"name":"Shenzhen Finance Institute","place":["School of Management and Economics, The Chinese University of Hong Kong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renda","family":"Shi","sequence":"additional","affiliation":[{"name":"Shenzhen Finance Institute","place":["School of Management and Economics, The Chinese University of Hong Kong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixi","family":"Song","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Southeast University","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunpeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong","place":["Shenzhen, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.jnca.2016.04.007","article-title":"Fraud detection system: a survey","volume":"68","author":"Abdallah","year":"2016","journal-title":"J. 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