{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:38:11Z","timestamp":1774967891960,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Increased digital banking operations have brought about a surge in suspicious activities, necessitating heightened real-time fraud detection systems. Conversely, traditional static approaches encounter challenges in maintaining privacy while adapting to new fraudulent trends. In this paper, we provide a unique approach to tackling those challenges by integrating VAE-QLSTM with Federated Learning (FL) in a semi-decentralized architecture, maintaining privacy alongside adapting to emerging malicious behaviors. The suggested architecture builds on the adeptness of VAE-QLSTM to capture meaningful representations of transactions, serving in abnormality detection. On the other hand, QLSTM combines quantum computational capability with temporal sequence modeling, seeking to give a rapid and scalable method for real-time malignancy detection. The designed approach was set up through TensorFlow Federated on two real-world datasets\u2014notably IEEE-CIS and European cardholders\u2014outperforming current strategies in terms of accuracy and sensitivity, achieving 94.5% and 91.3%, respectively. This proves the potential of merging VAE-QLSTM with FL to address fraud detection difficulties, ensuring privacy and scalability in advanced banking networks.<\/jats:p>","DOI":"10.3390\/bdcc9070185","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T07:38:27Z","timestamp":1752133107000},"page":"185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Adaptive, Privacy-Enhanced Real-Time Fraud Detection in Banking Networks Through Federated Learning and VAE-QLSTM Fusion"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6451-3441","authenticated-orcid":false,"given":"Hanae","family":"Abbassi","sequence":"first","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1938-621X","authenticated-orcid":false,"given":"Saida","family":"El Mendili","sequence":"additional","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8010-9206","authenticated-orcid":false,"given":"Youssef","family":"Gahi","sequence":"additional","affiliation":[{"name":"Engineering Sciences Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra 14000, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Faroukhi, A.Z., El Alaoui, I., Gahi, Y., and Amine, A. 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