{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:28:20Z","timestamp":1755224900995,"version":"3.43.0"},"reference-count":30,"publisher":"World Scientific Pub Co Pte Ltd","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,11,15]]},"abstract":"<jats:p> This paper presents a unique solution to improve supply chain financial risk management: \u201cBlockchain-based Double Feedback Fuzzy Neural Network ([Formula: see text]\u201d. This model aims to reduce credit risks effectively for Small and Medium sized firms(SMEs) in supply chain finance (SCF) by combining the advanced predictive powers of DFFNN with blockchain technology. With the help of blockchain technology, the model creates a transparent and secure supply chain financial system that stores and maintains financial data effectively by converting it into blocks and guarantees data safety in a permanent manner. Similarly, the DFFNN helps to process the data and allows accurate risk management to provide accurate risk ratings. The existing studies provide the essential foundation to build this suggested model. Most of the studies present effective techniques to solve the financial risk complexities, but the advanced risks are more intelligent than the existing approaches, which have a high potential to beat the traditional risk management systems and create a severe financial risk, which leads to a significant impact on the supply chain financial environment. Additionally, the present approach is a novel solution. Based on this, we direct the way to reach the expected possibilities, avoid financial risks and stabilize the financial sector. The simulation results highlight the improvement of the suggested [Formula: see text] model under risk prediction accuracy and processing efficiency. It also demonstrates the model\u2019s ability to improve risk management practices in financial institutions. <\/jats:p>","DOI":"10.1142\/s0218126625502573","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T04:16:23Z","timestamp":1740111383000},"source":"Crossref","is-referenced-by-count":0,"title":["Blockchain-Driven Double Feedback Fuzzy Neural Network for Enhanced Supply Chain Financial Risk Management"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5265-558X","authenticated-orcid":false,"given":"Fengqin","family":"Bao","sequence":"first","affiliation":[{"name":"Geely University of China, ChengDu, SiChuan 641423, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2466-9908","authenticated-orcid":false,"given":"Tong","family":"Liu","sequence":"additional","affiliation":[{"name":"Geely University of China, ChengDu, SiChuan 641423, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9751-4621","authenticated-orcid":false,"given":"Fang","family":"Chen","sequence":"additional","affiliation":[{"name":"Geely University of China, ChengDu, SiChuan 641423, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"S0218126625502573BIB001","doi-asserted-by":"publisher","DOI":"10.1002\/sd.2272"},{"key":"S0218126625502573BIB002","doi-asserted-by":"publisher","DOI":"10.1016\/j.pursup.2019.02.002"},{"key":"S0218126625502573BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2018.2861717"},{"key":"S0218126625502573BIB004","doi-asserted-by":"publisher","DOI":"10.1108\/IJPDLM-08-2014-0173"},{"key":"S0218126625502573BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2828651"},{"key":"S0218126625502573BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.techsoc.2024.102520"},{"key":"S0218126625502573BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.jik.2024.100490"},{"key":"S0218126625502573BIB008","doi-asserted-by":"publisher","DOI":"10.1108\/IJOPM-07-2020-0462"},{"key":"S0218126625502573BIB009","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2024.105323"},{"key":"S0218126625502573BIB010","doi-asserted-by":"publisher","DOI":"10.1017\/S1474745618000514"},{"key":"S0218126625502573BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.econmod.2024.106866"},{"key":"S0218126625502573BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbusres.2023.113869"},{"key":"S0218126625502573BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3317639"},{"key":"S0218126625502573BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.pursup.2019.02.002"},{"key":"S0218126625502573BIB015","doi-asserted-by":"publisher","DOI":"10.1007\/s42786-022-00040-1"},{"key":"S0218126625502573BIB016","doi-asserted-by":"publisher","DOI":"10.1057\/s41599-023-02273-x"},{"key":"S0218126625502573BIB017","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3007233"},{"first-page":"1440","volume-title":"6th IEEE Int. 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