{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:19:02Z","timestamp":1778545142076,"version":"3.51.4"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Financial transaction risk control is a cornerstone of intelligent finance platforms, yet existing approaches remain limited. Early frameworks modeled user behaviors independently, while later graph-based systems extracted handcrafted features from capital-flow networks. Although these methods improved detection, they struggle to capture fine-grained temporal dynamics and evolving topological patterns, and they depend heavily on manual feature engineering. In this work, we present a unified real-time dynamic graph learning framework that directly learns representations from raw streaming transaction graphs. Central to our design is a continuous-time, context-aware graph attention transformer (C2GAT), which models both higher-order structural dependencies and temporal patterns. We further decouple multi-role interaction paths and local neighborhood structures into dedicated subgraph modules, enabling complementary views of fraud behaviors. Evaluated on an industrial credit-cashback fraud detection scenario, our framework delivers substantial improvements in accuracy and false-alarm reduction over industry-standard baselines, while meeting stringent real-time latency requirements for deployment in large-scale financial systems.<\/jats:p>","DOI":"10.3389\/frai.2026.1774013","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T06:55:27Z","timestamp":1772088927000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Real-time dynamic graph learning with temporal attention for financial fraud detection"],"prefix":"10.3389","volume":"9","author":[{"given":"Jundong","family":"Chen","sequence":"first","affiliation":[{"name":"Finance and Banking Division, Southern Power Grid Digital Enterprise Technology (Guangdong) Co., Ltd.","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Yang","sequence":"additional","affiliation":[{"name":"Strategic Development Department, Southern Power Grid Capital Holding Co., Ltd.","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"177","DOI":"10.3390\/ai5010010","article-title":"Secure internet financial transactions: a framework integrating multi-factor authentication and machine learning","volume":"5","author":"Aburbeian","year":"2024","journal-title":"AI"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1643292","DOI":"10.3389\/frai.2025.1643292","article-title":"Enhancing credit card fraud detection using traditional and deep learning models with class imbalance mitigation","volume":"8","author":"Albalawi","year":"2025","journal-title":"Front. 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