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In order to minimize the financial risk brought by the credit sales of enterprises, this subject studies the intelligent optimization of enterprise financial account receivable management. BP neural network and <jats:italic>K<\/jats:italic>\u2010means clustering algorithm are used to evaluate the risk of account receivable and the owner\u2019s credit, respectively. The account balance accounts for 45.20% of the total amount, and the risk rating of accounts receivable is 4. The training result of BP neural network algorithm has high accuracy. With <jats:italic>K<\/jats:italic>\u2010means clustering algorithm, accurate evaluation of owner\u2019s credit can be achieved, which can provide reference for optimization of enterprise account receivable management mode.<\/jats:p>","DOI":"10.1155\/2024\/4961081","type":"journal-article","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T18:05:06Z","timestamp":1706378706000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intelligent Optimization Model of Enterprise Financial Account Receivable Management"],"prefix":"10.1155","volume":"2024","author":[{"given":"Yunxiang","family":"Peng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2863-1181","authenticated-orcid":false,"given":"Guixian","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,1,27]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.36995\/j.visiondefuturo.2021.25.02R.006.en"},{"key":"e_1_2_10_2_2","doi-asserted-by":"crossref","unstructured":"RamaneiT. 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Financial management early warning modeling analysis based on RBF neural network algorithm 2022 Second International Conference on Advanced Technologies in Intelligent Control Environment Computing & Communication Engineering (ICATIECE) December 2022 Bangalore India 1\u20135 https:\/\/doi.org\/10.1109\/ICATIECE56365.2022.10047262.","DOI":"10.1109\/ICATIECE56365.2022.10047262"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.4236\/me.2022.137053"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.22381\/am2120223"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/su122410287"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.31470\/2306-546X-2020-47-75-81"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.33062\/ajb.v5i2.382"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.24136\/oc.2023.021"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.17150\/2500-2759.2019.29(1).123-131"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.22381\/am2120224"},{"key":"e_1_2_10_17_2","doi-asserted-by":"crossref","unstructured":"ZhongJ. 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