{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T02:17:08Z","timestamp":1777861028161,"version":"3.51.4"},"reference-count":83,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Credit card fraud detection remains a challenging research problem due to the class imbalance issue caused by the rarity of fraudulent transactions. Classical oversampling techniques such as SMOTE, ADASYN and their variants help balance data but do not reflect the nonlinear structure of real\u2010world fraud, leading to poor generalization. Recent state\u2010of\u2010the\u2010art hybrid frameworks that combine deep generative models and ensemble learning improve performance but treat representation learning, augmentation and fusion as disconnected stages. To address these limitations, we propose a unified multistage framework that integrates representation learning, generative augmentation and intelligent ensemble fusion. Our framework first extracts autoencoder\u2010based latent representations to capture discriminative and interpretable features; then, a label\u2010conditioned VAE\u2010GAN uses these embeddings to generate realistic synthetic fraud samples; finally, the enriched features are projected into a fusion space and classified using a pool of diverse learners, whose outputs are consolidated through an embedding\u2010aware intelligent ensemble and a meta\u2010ensemble layer. We benchmark the framework against two categories of baselines: oversampling\u2010based methods and state\u2010of\u2010the\u2010art hybrid fraud detection systems. Experiments on the European cardholder dataset show that our approach achieves a macro F1\u2010score of 95.15% and balanced accuracy of 92.85%, outperforming both baseline categories by 2.8%. Additional experiments on the IEEE\u2010CIS Fraud Detection dataset further validate the generalizability of the proposed framework on large\u2010scale, heterogeneous and feature\u2010rich fraud data. The results demonstrate that the proposed framework not only improves detection accuracy under severe imbalances but also maintains interpretability, offering a robust and scalable foundation for reliable financial risk control.<\/jats:p>","DOI":"10.1111\/exsy.70256","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:45:44Z","timestamp":1775781944000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Imbalance\u2010Aware Credit Card Fraud Detection Using Multi\u2010Autoencoders and Generative Ensemble Learning"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8146-8032","authenticated-orcid":false,"given":"Sultan","family":"Alharbi","sequence":"first","affiliation":[{"name":"University of Technology Sydney  Sydney New South Wales Australia"},{"name":"Umm Al Qura University  Makkah Saudi Arabia"}]},{"given":"Khalid","family":"Alahmadi","sequence":"additional","affiliation":[{"name":"University of Technology Sydney  Sydney New South Wales Australia"},{"name":"King Abdulaziz University  Jeddah Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9582-3445","authenticated-orcid":false,"given":"Xianzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney  Sydney New South Wales Australia"}]}],"member":"311","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.5267\/j.ijdns.2023.6.003"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1643292"},{"key":"e_1_2_9_4_1","unstructured":"Almalki F. andM.Masud.2025.\u201cFinancial Fraud Detection Using Explainable AI and Stacking Ensemble Methods.\u201darXiv Preprint arXiv:2505.10050.https:\/\/arxiv.org\/abs\/2505.10050."},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3320072"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3519335"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2023.9020035"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.1992.10475879"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/computers14100437"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/IATMSI60426.2024.10503258"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124127"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.04.057"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"Chen T. andC.Guestrin.2016.Xgboost: A Scalable Tree Boosting System. 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