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Yet, the challenges of high dimensionality and noisy financial data often undermine the effectiveness of existing financial fraud detection systems. To address these issues, this study proposes SISAE\u2010METADES, a novel framework that integrates a supervised input\u2010enhanced stacked autoencoder (SISAE) with a meta\u2010learning\u2013based dynamic ensemble selection (METADES) strategy. Unlike conventional stacked autoencoders, SISAE concatenates the original input at each encoding stage and incorporates label supervision, thereby learning task\u2010relevant and class\u2010discriminative representations. These enriched deep features improve both the diversity and competence of base classifiers and enable METADES to achieve more reliable local competence estimation. We validate the proposed framework using financial statement data from Chinese A\u2010share listed companies (2005\u20132023), covering 71 indicators. Experimental results show that SISAE\u2010METADES significantly outperforms standalone SISAE, traditional METADES, and several state\u2010of\u2010the\u2010art baselines. In particular, it achieves substantial improvements in accuracy, recall, and F1\u2010score, underscoring the robustness and effectiveness of combining supervised deep representation learning with dynamic ensemble selection for financial fraud detection. These findings highlight the framework\u2019s practical significance in reducing investor losses, strengthening market confidence, and promoting the stability of the financial system.<\/jats:p>","DOI":"10.1155\/int\/8869784","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T06:17:43Z","timestamp":1760681863000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Financial Fraud Detection via SISAE\u2010METADES: A Supervised Deep Representation and Dynamic Ensemble Approach"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4933-5349","authenticated-orcid":false,"given":"Chang","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5807-426X","authenticated-orcid":false,"given":"Sheng","family":"Fang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7782-2594","authenticated-orcid":false,"given":"Fangsu","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2334-7099","authenticated-orcid":false,"given":"Zongmei","family":"Mu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1111\/acfi.13159"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2021.102477"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaccpubpol.2010.06.006"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116429"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.5539\/ijef.v7n7p178"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1100.1174"},{"key":"e_1_2_10_7_2","doi-asserted-by":"crossref","unstructured":"FournierQ.andAloiseD. 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